Patent Publication Number: US-2018035924-A1

Title: Accelerometer signal change as a measure of patient functional status

Description:
RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 62/370,138, filed Aug. 2, 2016, incorporated by reference herein. 
    
    
     FIELD 
     The disclosure relates generally to medical device systems, and more particularly to medical device systems configured for determining patient functional status based on accelerometer-generated data. 
     BACKGROUND 
     Implantable medical devices (IMDs) and external, e.g., wearable, medical devices, including implantable pacemakers and implantable cardioverter-defibrillators (ICDs), record cardiac electrogram (EGM) signals for sensing cardiac events, e.g., P-waves and R-waves. IMDs detect episodes of bradycardia, tachycardia and/or fibrillation from the sensed cardiac events, and respond to the episodes as needed with pacing therapy or high-voltage anti-tachyarrhythmia shocks, e.g., cardioversion or defibrillation shocks. These and other medical devices may include, or be part of a system that includes, sensors that generate other physiological-based signals, such as signals that vary based on patient movement or activity, cardiovascular pressure, blood oxygen saturation, edema, or thoracic impedance. 
     SUMMARY 
     In general, this disclosure is directed to techniques for determining patient functional status based on accelerometer-generated data. Although not so limited, a number of example implementations of such techniques are contemplated, such as: 
     A medical device system that includes or comprises: accelerometer circuitry configured to generate a plurality of signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal; and processing circuitry configured to: calculate a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal. 
     A method that includes or comprises: generating, by a medical device system, a plurality of signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal; and calculating, by the medical device system, a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal. 
     An implantable medical device that includes or comprises: communication circuity configured to establish a communication link and transfer data between the IMD intra-corpus and a computing device extra-corpus; accelerometer circuitry configured to generate a plurality of signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal; and processing circuitry configured to: calculate a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and activate the communication circuity to transmit the patient-specific functional status parameter from the IMD to the computing device. 
     A method that includes or comprises: by an implantable medical device, intra-corpus, generating a plurality of signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal; calculating a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and transmitting the patient-specific functional status parameter to a computing device extra-corpus. 
     In other examples, a medical device system comprises means for performing any of the methods or techniques described herein. 
     In other examples, non-transitory computer-readable media comprise program instructions that, when executed by processing circuitry of a medical device system, cause the medical device system to perform any of the methods or techniques described herein. 
     This summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the apparatus and methods described in detail within the accompanying drawings and description below. The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a conceptual drawing illustrating an example medical device system in conjunction with a patient. 
         FIG. 2  is a conceptual drawing illustrating another example medical device system in conjunction with a patient. 
         FIG. 3  is a perspective drawing illustrating an example configuration of the implantable cardiac monitor of  FIG. 2 . 
         FIGS. 4A-4C  is a front-view, side-view, and top-view conceptual drawings, respectively, illustrating another example medical device system in conjunction with a patient. 
         FIG. 5  is a conceptual drawing illustrating another example medical device system in conjunction with a patient. 
         FIG. 6  is a conceptual diagram illustrating an example configuration of the intracardiac pacing device of  FIGS. 4A-5 . 
         FIG. 7  is a functional block diagram illustrating an example configuration of an implantable medical device. 
         FIG. 8  is a functional block diagram illustrating an example configuration of an external device configured to communicate with one or more implantable medical devices. 
         FIG. 9  is a functional block diagram illustrating an example system that includes remote computing devices, such as a server and one or more other computing devices, that are connected to an implantable medical device and/or external device via a network. 
         FIG. 10  is a flowchart illustrating a first example method for determining patient functional status based on accelerometer-generated data in accordance with the disclosure. 
         FIG. 11  is a conceptual diagram illustrating sagittal, vertical and transverse axes in a three-dimensional coordinate system. 
         FIG. 12  is a plot illustrating sagittal, vertical and transverse axis signals produced by an accelerometer during a series of sit-stand and stand-sit movements. 
         FIG. 13  is a plot illustrating the slopes of segments of the sagittal axis signal of  FIG. 12 . 
         FIG. 14  is a conceptual diagram illustrating a change in several characteristics of the sagittal axis signal of  FIG. 12  over the series of sit-stand and stand-sit movements. 
         FIG. 15  is a plot illustrating several characteristic of the sagittal axis signal of  FIG. 12 . 
         FIG. 16  is a flowchart illustrating a second example method for determining patient functional status based on accelerometer-generated data in accordance with the disclosure. 
         FIG. 17  is a plot illustrating characteristics of the vertical axis signal of  FIG. 12 . 
         FIG. 18  is a plot illustrating characteristics of the vertical axis signal of  FIG. 17 . 
         FIGS. 19-28  each is a plot of a series of plots illustrating sagittal, vertical and transverse axis signals produced by an accelerometer during at least one sit-stand movement. 
         FIG. 29  is a flowchart illustrating a third example method for determining patient functional status based on accelerometer-generated data in accordance with the disclosure. 
         FIGS. 30-33  illustrate a sequence of graphical user interfaces for acquiring timestamps to mark a window for determining patient functional status based on accelerometer-generated data in accordance with the disclosure. 
         FIG. 34  is a functional block diagram illustrating an example communication sequence between an implantable medical device and an external device in accordance with the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Implantable medical devices (IMDs) and external, e.g., wearable, medical devices, including implantable pacemakers and implantable cardioverter-defibrillators (ICDs), record cardiac electrogram (EGM) signals for sensing cardiac events, e.g., P-waves and R-waves. IMDs detect episodes of bradycardia, tachycardia and/or fibrillation from the sensed cardiac events, and respond to the episodes as needed with pacing therapy or high-voltage anti-tachyarrhythmia shocks, e.g., cardioversion or defibrillation shocks. These and other medical devices may include, or be part of a system that includes, sensors that generate other physiological-based signals, such as signals that vary based on patient movement or activity, cardiovascular pressure, blood oxygen saturation, edema, or thoracic impedance. According to the features or aspects of this disclosure, one or more of such signals may be leveraged to provide on objective measure of patient functional status. 
     For example, a medical device system according to certain features or aspects of this disclosure includes accelerometer circuitry configured to generate a number of signals including a sagittal (frontal) axis signal, as well as processing circuitry configured to calculate a patient-specific functional status parameter associated with a Sit-To-Stand test from the sagittal axis signal. Such an implementation may, among other things, provide an objective measure of change (or not) in well-being to help guide therapies, because a patient-specific functional status parameter associated with a Sit-To-Stand test can help determine whether health is improving, declining, or stable. Although not so limited, an appreciation of the various aspects of the present disclosure may be gained from the following discussion in connection with the drawings. 
     For example,  FIG. 1  is a conceptual drawing illustrating an example medical device system  8 A in conjunction with a patient  14 A. Medical device system  8 A is an example of a medical device system configured to implement the techniques described herein for determining patient functional status based on accelerometer-generated data. In the illustrated example, medical device system  8 A includes an implantable medical device (IMD)  10 A coupled to a ventricular lead  20  and an atrial lead  21 . IMD  10 A is an implantable cardioverter-defibrillator (ICD) capable of delivering pacing, cardioversion and defibrillation therapy to the heart  16 A of a patient  14 A, and will be referred to as ICD  10 A hereafter. 
     Ventricular lead  20  and atrial lead  21  are electrically coupled to ICD  10 A and extend into the patient&#39;s heart  16 A. Ventricular lead  20  includes electrodes  22  and  24  shown positioned on the lead in the patient&#39;s right ventricle (RV) for sensing ventricular EGM signals and pacing in the RV. Atrial lead  21  includes electrodes  26  and  28  positioned on the lead in the patient&#39;s right atrium (RA) for sensing atrial EGM signals and pacing in the RA. 
     Ventricular lead  20  additionally carries a high voltage coil electrode  42 , and atrial lead  21  carries a high voltage coil electrode  44 , used to deliver cardioversion and defibrillation shocks. The term “anti-tachyarrhythmia shock” may be used herein to refer to both cardioversion shocks and defibrillation shocks. In other examples, ventricular lead  20  may carry both of high voltage coil electrodes  42  and  44 , or may carry a high voltage coil electrode in addition to those illustrated in the example of  FIG. 1 . 
     ICD  10 A may use both ventricular lead  20  and atrial lead  21  to acquire cardiac electrogram (EGM) signals from patient  14 A and to deliver therapy in response to the acquired data. Medical device system  8 A is shown as having a dual chamber ICD configuration, but other examples may include one or more additional leads, such as a coronary sinus lead extending into the right atrium, through the coronary sinus and into a cardiac vein to position electrodes along the left ventricle (LV) for sensing LV EGM signals and delivering pacing pulses to the LV. In other examples, a medical device system may be a single chamber system, or otherwise not include atrial lead  21 . 
     Processing circuitry, sensing circuitry, and other circuitry configured for performing the techniques described herein are housed within a sealed housing  12 . Housing  12  (or a portion thereof) may be conductive so as to serve as an electrode for pacing or sensing or as an active electrode during defibrillation. As such, housing  12  is also referred to herein as “housing electrode”  12 . 
     ICD  10 A may transmit EGM signal data and cardiac rhythm episode data acquired by ICD  10 A, as well as data regarding delivery of therapy by ICD  10 A, as well as data in manipulated and/or in raw form, possibly compressed, encoded, and/or the like, associated with patient functional status as derived from accelerometer-generated data, to an external device  30 A. External device  30 A may be a computing device, e.g., used in a home, ambulatory, clinic, or hospital setting, to communicate with ICD  10 A via wireless telemetry. External device  30 A may be coupled to a remote patient monitoring system, such as Carelink®, available from Medtronic plc, of Dublin, Ireland. External device  30 A may be, as examples, a programmer, external monitor, or consumer device, e.g., a smartphone, such as the iPhone® by Apple Inc. of Cupertino, Calif. 
     External device  30 A may be used to program commands or operating parameters into ICD  10 A for controlling its functioning, e.g., when configured as a programmer for ICD  10 A, or when configured to provide timestamp data for calculating a patient-specific functional status parameter associated with a Sit-To-Stand test. External device  30 A may be used to interrogate ICD  10 A to retrieve data, including device operational data as well as physiological data accumulated in IMD memory, such as data associated with a patient-specific functional status parameter associated with a Sit-To-Stand test. The interrogation may be automatic, e.g., according to a schedule, or in response to a remote or local user command. Programmers, external monitors, and consumer devices are examples of external devices  30 A that may be used to interrogate ICD  10 A. Examples of communication techniques used by ICD  10 A and external device  30 A include radiofrequency (RF) telemetry, which may be an RF link established via Bluetooth, WiFi, or medical implant communication service (MICS). 
     In some examples, as illustrated in  FIG. 1 , medical device system  8 A may also include a pressure-sensing IMD  50 . In the illustrated example, pressure-sensing IMD  50  is implanted in the pulmonary artery of patient  14 A. In some examples, one or more pressure-sensing IMDs  50  may additionally or alternatively be implanted within a chamber of heart  16 A, or generally at other locations in the circulatory system. 
     In one example, pressure-sensing IMD  50  is configured to sense blood pressure of patient  14 A. For example, pressure-sensing IMD  50  may be arranged in the pulmonary artery and be configured to sense the pressure of blood flowing from the right ventricle outflow tract (RVOT) from the right ventricle through the pulmonary valve to the pulmonary artery. Pressure-sensing IMD  50  may therefore directly measure pulmonary artery diastolic pressure (PAD) of patient  14 A. The PAD value is a pressure value that can be employed in patient monitoring. For example, PAD may be used as a basis for evaluating congestive heart failure in a patient. 
     In other examples, however, pressure-sensing IMD  50  may be employed to measure blood pressure values other than PAD. For example, pressure-sensing IMD  50  may be arranged in right ventricle  28  of heart  14  to sense RV systolic or diastolic pressure, or may sense systolic or diastolic pressures at other locations of the cardiovascular system, such as within the pulmonary artery. As shown in  FIG. 1 , pressure-sensing IMB  50  is positioned in the main trunk of pulmonary artery  39 . In other examples, a sensor, such as pressure-sensing IMB  50  may be either positioned in the right or left pulmonary artery beyond the bifurcation of the pulmonary artery. 
     Moreover, the placement of pressure-sensing IMD  50  is not restricted necessarily to the pulmonary side of the circulation. The pressure-sensing IMB  50  could potentially be placed in the systemic side of the circulation. For example, under certain conditions and with appropriate safety measures, pressure-sensing IMD  50  could even be placed in the left atrium, left ventricle, or aorta. Additionally, pressure-sensing IMD  50  is not restricted to placement within the cardiovascular system. For example, the pressure-sensing IMB  50  might be placed in the renal circulation. Placement of pressure-sensing IMB  50  in the renal circulation may be beneficial, for example, to monitor the degree of renal insufficiency in the patient based on the monitoring of pressure or some other indication of renal circulation by pressure-sensing IMD  50 . 
     In some examples, pressure-sensing IMD  50  includes a pressure sensor configured to respond to the absolute pressure inside the pulmonary artery of patient  14 A. Pressure-sensing IMD  50  may be, in such examples, any of a number of different types of pressure sensors. One form of pressure sensor that may be useful for measuring blood pressure is a capacitive pressure sensor. Another example pressure sensor is an inductive sensor. In some examples, pressure-sensing IMD  50  may also comprise a piezoelectric or piezoresistive pressure transducer. In some examples, pressure-sensing IMD  50  may comprise a flow sensor. 
     In one example, pressure-sensing IMD  50  comprises a leadless pressure sensor including capacitive pressure sensing elements configured to measure blood pressure within the pulmonary artery. Pressure-sensing IMD  50  may be in wireless communication with ICD  10 A and/or external device  30 A, e.g., in order to transmit blood pressure measurements to one or both of the devices. Pressure-sensing IMD  50  may employ, e.g., radio frequency (RF) or other telemetry techniques for communicating with ICD  10 A and other devices, including, e.g., external device  30 A. In another example, pressure-sensing IMD  50  may include a tissue conductance communication (TCC) system by which the device employs tissue of patient  14 A as an electrical communication medium over which to send and receive information to and from ICD  10 A and/or external device  30 A. 
     Medical device system  8 A is an example of a medical device system configured for determining patient functional status based on accelerometer-generated data. Such techniques as contemplated may be performed by processing circuitry of medical device system  8 A, such as processing circuitry of one or both of ICD  10 A and external device  30 A, individually, or collectively, as discussed in further detail below. Other example medical device systems that may be configured to implement the techniques are described with respect to  FIGS. 2-9 . Although described herein primarily in the context of implantable medical devices generating signals and, in some examples, delivering therapy, a medical device system that implements the techniques described in this disclosure may additionally or alternatively include an external medical device, e.g., a smartphone, configured to at least generate timestamp data for measuring or determining patient functional status based on accelerometer-generated data 
       FIG. 2  is a conceptual drawing illustrating another example medical device system  8 B in conjunction with a patient  14 B. Medical device system  8 B is another example of a medical device system configured to implement the techniques described herein for determining patient functional status based on accelerometer-generated data. In the illustrated example, medical device system  8 B includes an IMD  10 B and an external device  30 B. 
     IMD  10 B is an insertable cardiac monitor (ICM) capable of sensing and recording cardiac EGM signals from a position outside of heart  16 B, and will be referred to as ICM  10 B hereafter. Further, ICM  10 B is capable of implementing one or more techniques for determining patient functional status based on accelerometer-generated data in accordance with the present disclosure. In some examples, ICM  10 B includes or is coupled to one or more additional sensors that generate one or more other physiological signals, such as signals that vary based on patient motion and/or posture, blood flow, or respiration. ICM  10 B may be implanted outside of the thorax of patient  14 B, e.g., subcutaneously or submuscularly, such as the pectoral location illustrated in  FIG. 2 . In some examples, ICM  10 B may take the form of a Reveal LINQ™ ICM, available from Medtronic plc, of Dublin, Ireland. 
     External device  30 B may be configured in a manner substantially similar to that described above with respect to external device  30 A and  FIG. 1 . External device  30 B may wirelessly communicate with ICM  10 B, e.g., to program the functionality of the ICM, and to retrieve recorded physiological signals and/or patient parameter values or other data derived from such signals from the ICM. Both ICM  10 B and external device  30 B include processing circuitry, and the processing circuitry of either or both device may perform the techniques described herein for determining patient functional status based on accelerometer-generated data, as discussed in further detail below. 
     Although not illustrated in the example of  FIG. 2 , a medical device system configured to implement the techniques of this disclosure may include one or more implanted or external medical devices in addition to or instead of ICM  10 B. For example, a medical device system may include a pressure sensing IMD  50 , vascular ICD (e.g., ICD  10 A of  FIG. 1 ), extravascular ICD (e.g., ICD  10 C of  FIGS. 4A-5 ), or cardiac pacemaker (e.g., IPD  10 D of  FIGS. 4A-6  or a cardiac pacemaker implanted outside the heart but coupled to intracardiac or epicardial leads). One or more such devices may generate accelerometer signals, and include processing circuitry configured to perform, in whole or in part, the techniques described herein for determining patient functional status based on accelerometer-generated data. The implanted devices may communicate with each other and/or an external device  30 , and one of the implanted or external devices may ultimately calculate a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of a sagittal axis signal, a vertical axis signal and a transverse axis signal. 
       FIG. 3  is a conceptual drawing illustrating an example configuration of ICM  10 B. In the example shown in  FIG. 3 , ICM  300  may be embodied as a monitoring device having housing  62 , proximal electrode  64  and distal electrode  66 . Housing  62  may further comprise first major surface  68 , second major surface  70 , proximal end  72 , and distal end  74 . Housing  62  encloses electronic circuitry located inside the ICM  10 B and protects the circuitry contained therein from body fluids. Electrical feedthroughs provide electrical connection of electrodes  64  and  66 . 
     In the example shown in  FIG. 3 , ICM  10 B is defined by a length L, a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D. In one example, the geometry of the ICM  10 B—in particular a width W greater than the depth D—is selected to allow ICM  10 B to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during insertion. For example, the device shown in  FIG. 3  includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion. For example, in one example the spacing between proximal electrode  64  and distal electrode  66  may range from 30 millimeters (mm) to 55 mm, 35 mm to 55 mm, and from 40 mm to 55 mm and may be any range or individual spacing from 25 mm to 60 mm. In addition, ICM  10 B may have a length L that ranges from 30 mm to about 70 mm. In other examples, the length L may range from 40 mm to 60 mm, 45 mm to 60 mm and may be any length or range of lengths between about 30 mm and about 70 mm. In addition, the width W of major surface 68 may range from 3 mm to 10 mm and may be any single or range of widths between 3 mm and 10 mm. The thickness of depth D of ICM  10 B may range from 2 mm to 9 mm. In other examples, the depth D of ICM  10 B may range from 2 mm to 5 mm and may be any single or range of depths from 2 mm to 9 mm. In addition, ICM  10 B according to an example of the present disclosure is has a geometry and size designed for ease of implant and patient comfort. Examples of ICM  10 B described in this disclosure may have a volume of three cubic centimeters (cm) or less, 1.5 cubic cm or less or any volume between three and 1.5 cubic centimeters. And, as discussed in further detail below, it is contemplated that an axis coincident with an axis along D may correspond to a sagittal axis of an accelerometer, and that a sagittal axis signal(s) can be leveraged for measuring or determining patient functional status, as part of a SST (Sit-To-Stand) performance test for example. This is because 3D accelerometers in the ICM  10 B, for example, which is implanted in the chest, and are relatively stationary over the lifetime of the implant. The stationary chest location presents an opportunity to monitor changes in the upper body that occur during various activities. As a person gets in and out of a chair for example the upper body has a reproducible motion (similar to a “bowing” motion) that may be identified with signals produced by the accelerometers. 
     In the example shown in  FIG. 3 , once inserted within the patient, the first major surface  68  faces outward, toward the skin of the patient while the second major surface  70  is located opposite the first major surface  68 . In addition, in the example shown in  FIG. 3 , proximal end  72  and distal end  74  are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient. ICM  10 B, including instrument and method for inserting ICM  10 B is described, for example, in U.S. Patent Publication No. 2014/0276928, incorporated herein by reference in its entirety. 
     Proximal electrode  64  and distal electrode  66  are used to sense cardiac signals, e.g. ECG signals, intra-thoracically or extra-thoracically, which may be sub-muscularly or subcutaneously. ECG signals may be stored in a memory of the ICM  10 B, and ECG data may be transmitted via integrated antenna  82  to another medical device, which may be another implantable device or an external device, such as external device  30 B. In some example, electrodes  64  and  66  may additionally or alternatively be used for sensing any bio-potential signal of interest, which may be, for example, an EGM, EEG, EMG, or a nerve signal, from any implanted location. 
     In the example shown in  FIG. 3 , proximal electrode  64  is in close proximity to the proximal end  72  and distal electrode  66  is in close proximity to distal end  74 . In this example, distal electrode  66  is not limited to a flattened, outward facing surface, but may extend from first major surface  68  around rounded edges  76  and/or end surface  78  and onto the second major surface  70  so that the electrode  66  has a three-dimensional curved configuration. In the example shown in  FIG. 3 , proximal electrode  64  is located on first major surface  68  and is substantially flat, outward facing. However, in other examples proximal electrode  64  may utilize the three-dimensional curved configuration of distal electrode  66 , providing a three-dimensional proximal electrode (not shown in this example). Similarly, in other examples distal electrode  66  may utilize a substantially flat, outward facing electrode located on first major surface  68  similar to that shown with respect to proximal electrode  64 . 
     The various electrode configurations allow for configurations in which proximal electrode  64  and distal electrode  66  are located on both first major surface  68  and second major surface  70 . In other configurations, such as that shown in  FIG. 3 , only one of proximal electrode  64  and distal electrode  66  is located on both major surfaces  68  and  70 , and in still other configurations both proximal electrode  64  and distal electrode  66  are located on one of the first major surface  68  or the second major surface  70  (i.e., proximal electrode  64  located on first major surface  68  while distal electrode  66  is located on second major surface  70 ). In another example, ICM  10 B may include electrodes on both major surface  68  and  70  at or near the proximal and distal ends of the device, such that a total of four electrodes are included on ICM  10 B. Electrodes  64  and  66  may be formed of a plurality of different types of biocompatible conductive material, e.g. stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride. 
     In the example shown in  FIG. 3 , proximal end  72  includes a header assembly  80  that includes one or more of proximal electrode  64 , integrated antenna  82 , anti-migration projections  84 , and/or suture hole  86 . Integrated antenna  82  is located on the same major surface (i.e., first major surface  68 ) as proximal electrode  64  and is also included as part of header assembly  80 . Integrated antenna  82  allows ICM  10 B to transmit and/or receive data. In other examples, integrated antenna  82  may be formed on the opposite major surface as proximal electrode  64 , or may be incorporated within the housing  82  of ICM  10 B. In the example shown in  FIG. 3 , anti-migration projections  84  are located adjacent to integrated antenna  82  and protrude away from first major surface  68  to prevent longitudinal movement of the device. In the example shown in  FIG. 3 , anti-migration projections  84  includes a plurality (e.g., nine) small bumps or protrusions extending away from first major surface  68 . As discussed above, in other examples anti-migration projections  84  may be located on the opposite major surface as proximal electrode  64  and/or integrated antenna  82 . In addition, in the example shown in  FIG. 3  header assembly  80  includes suture hole  86 , which provides another means of securing ICM  10 B to the patient to prevent movement following insert. In the example shown, suture hole  86  is located adjacent to proximal electrode  64 . In one example, header assembly  80  is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of ICM  10 B. 
       FIGS. 4A-4C  are front-view, side-view, and top-view conceptual drawings, respectively, illustrating another example medical device system  8 C in conjunction with a patient  14 C. Medical device system  8 C is another example of a medical device system configured to implement the techniques described herein for determining patient functional status based on accelerometer-generated data. 
     In the illustrated example, medical device system  8 C includes an extracardiovascular ICD system  100 A implanted within a patient  14 C. ICD system  100 A includes an IMB  10 C, which is an ICD and is referred to hereafter as ICD  10 C, connected to at least one implantable cardiac defibrillation lead  102 A. ICD  10 C is configured to deliver high-energy cardioversion or defibrillation pulses to a patient&#39;s heart  16 C when atrial or ventricular fibrillation is detected. Cardioversion shocks are typically delivered in synchrony with a detected R-wave when fibrillation detection criteria are met. Defibrillation shocks are typically delivered when fibrillation criteria are met, and the R-wave cannot be discerned from signals sensed by ICD  10 C. 
     ICD  10 C is implanted subcutaneously or submuscularly on the left side of patient  14 C above the ribcage. Defibrillation lead  102 A may be implanted at least partially in a substernal location, e.g., between the ribcage and/or sternum  110  and heart  16 C. In one such configuration, a proximal portion of lead  102 A extends subcutaneously from ICD  10 C toward sternum  110  and a distal portion of lead  102 A extends superior under or below the sternum  110  in the anterior mediastinum  112  ( FIG. 4C ). The anterior mediastinum  112  is bounded laterally by the pleurae  116  ( FIG. 1C ), posteriorly by the pericardium  114  ( FIG. 4C ), and anteriorly by the sternum  110 . In some instances, the anterior wall of the anterior mediastinum may also be formed by the transversus thoracis and one or more costal cartilages. The anterior mediastinum includes a quantity of loose connective tissue (such as areolar tissue), some lymph vessels, lymph glands, substernal musculature (e.g., transverse thoracic muscle), branches of the internal thoracic artery, and the internal thoracic vein. In one example, the distal portion of lead  102 A extends along the posterior side of the sternum  110  substantially within the loose connective tissue and/or substernal musculature of the anterior mediastinum. Lead  102 A may be at least partially implanted in other intrathoracic locations, e.g., other non-vascular, extra-pericardial locations, including the gap, tissue, or other anatomical features around the perimeter of and adjacent to, but not attached to, the pericardium or other portion of the heart and not above the sternum  110  or ribcage. 
     In other examples, lead  102 A may be implanted at other extracardiovascular locations. For example, defibrillation lead  102 A may extend subcutaneously above the ribcage from ICD  10 C toward a center of the torso of patient  14 C, bend or turn near the center of the torso, and extend subcutaneously superior above the ribcage and/or sternum  110 . Defibrillation lead  102 A may be offset laterally to the left or the right of the sternum  110  or located over the sternum  110 . Defibrillation lead  102 A may extend substantially parallel to the sternum  110  or be angled lateral from the sternum  110  at either the proximal or distal end. 
     Defibrillation lead  102 A includes an insulative lead body having a proximal end that includes a connector  104  configured to be connected to ICD  10 C and a distal portion that includes one or more electrodes. Defibrillation lead  102 A also includes one or more conductors that form an electrically conductive path within the lead body and interconnect the electrical connector and respective ones of the electrodes. 
     Defibrillation lead  102 A includes a defibrillation electrode that includes two sections or segments  106 A and  106 B, collectively (or alternatively) defibrillation electrode  106 . The defibrillation electrode  106  is toward the distal portion of defibrillation lead  102 A, e.g., toward the portion of defibrillation lead  102 A extending along the sternum  110 . Defibrillation lead  102 A is placed below and/or along sternum  110  such that a therapy vector between defibrillation electrodes  106 A or  106 B and a housing electrode formed by or on ICD  10 C (or other second electrode of the therapy vector) is substantially across a ventricle of heart  16 C. The therapy vector may, in one example, be viewed as a line that extends from a point on defibrillation electrode  106  (e.g., a center of one of the defibrillation electrode sections  106 A or  106 B) to a point on the housing electrode of ICD  10 C. Defibrillation electrode  106  may, in one example, be an elongated coil electrode. Defibrillation lead  102 A may also include one or more sensing electrodes, such as sensing electrodes  108 A and  108 B (individually or collectively, “sensing electrode(s)  108 ”), located along the distal portion of defibrillation lead  102 A. In the example illustrated in  FIG. 4A  and  FIG. 4B , sensing electrodes  108 A and  108 B are separated from one another by defibrillation electrode  106 A. In other examples, however, sensing electrodes  108 A and  108 B may be both distal of defibrillation electrode  106  or both proximal of defibrillation electrode  106 . In other examples, lead  102 A may include more or fewer electrodes at various locations proximal and/or distal to defibrillation electrode  106 . In the same or different examples, ICD  10 C may include one or more electrodes on another lead (not shown). 
     ICD system  100 A may sense electrical signals via one or more sensing vectors that include combinations of electrodes  108 A and  108 B and the housing electrode of ICD  10 C. In some instances, ICD  10 C may sense cardiac electrical signals using a sensing vector that includes one of the defibrillation electrode sections  106 A and  106 B and one of sensing electrodes  108 A and  108 B or the housing electrode of ICD  9 . The sensed electrical intrinsic signals may include electrical signals generated by cardiac muscle and indicative of depolarizations and repolarizations of heart  16 C at various times during the cardiac cycle. ICD  10 C analyzes the electrical signals sensed by the one or more sensing vectors to detect tachyarrhythmia, such as ventricular tachycardia or ventricular fibrillation. In response to detecting the tachyarrhythmia, ICD  10 C may begin to charge a storage element, such as a bank of one or more capacitors, and, when charged, deliver one or more defibrillation pulses via defibrillation electrode  106  of defibrillation lead  102 A if the tachyarrhythmia is still present. 
     Medical device system  8 C also includes an IMD  10 D, which is implanted within heart  16 C and configured to deliver cardiac pacing to the heart, e.g., is an intracardiac pacing device (IPD). IMD  10 D is referred to as IPD  10 D hereafter. In the illustrated example, IPD  10 D is implanted within the right ventricle of heart  16 C. However, in other examples, system  8 C may additionally or alternatively include one or more IPDs  10 D within other chambers of heart  16 C, or similarly configured pacing devices attached to an external surface of heart  16 C (e.g., in contact with the epicardium) such that the pacing device is disposed outside of heart  16 C. 
     IPD  10 D is configured to sense electrical activity of heart  16 C and deliver pacing therapy, e.g., bradycardia pacing therapy, cardiac resynchronization therapy (CRT), anti-tachycardia pacing (ATP) therapy, and/or post-shock pacing, to heart  16 C. IPD  10 D may be attached to an interior wall of heart  16 C via one or more fixation elements that penetrate the tissue. These fixation elements may secure IPD  10 D to the cardiac tissue and retain an electrode (e.g., a cathode or an anode) in contact with the cardiac tissue. 
     IPD  10 D may be capable sensing electrical signals using the electrodes carried on the housing of IPD  10 D. These electrical signals may be electrical signals generated by cardiac muscle and indicative of depolarizations and repolarizations of heart  16 C at various times during the cardiac cycle. IPD  10 D may analyze the sensed electrical signals to detect bradycardia and tachyarrhythmias, such as ventricular tachycardia or ventricular fibrillation. In response to detecting bradycardia, IPD  10 D may deliver bradycardia pacing via the electrodes of IPD  10 D. In response to detecting tachyarrhythmia, IPD  10 D may, e.g., depending on the type of tachyarrhythmia, deliver ATP therapy via the electrodes of IPD  10 D. In some examples, IPD  10 D may deliver post-shock pacing in response to determining that another medical device, e.g., ICD  10 C, delivered an anti-tachyarrhythmia shock. 
     IPD  10 D and ICD  10 C may be configured to coordinate their arrhythmia detection and treatment activities. In some examples IPD  10 D and ICD  10 C may be configured to operate completely independently of one another. In such a case, IPD  10 D and ICD  10 C are not capable of establishing telemetry communication sessions with one another to exchange information about sensing and/or therapy using one-way or two-way communication. Instead, each of IPD  10 D and ICD  10 C analyze the data sensed via their respective electrodes to make tachyarrhythmia detection and/or therapy decisions. As such, each device does not know if the other will detect the tachyarrhythmia, if or when it will provide therapy, and the like. In some examples, IPD  10 D may be configured to detect anti-tachyarrhythmia shocks delivered by ICD system  100 A, which may improve the coordination of therapy between subcutaneous ICD  10 C and IPD  10 D without requiring device-to-device communication. In this manner, IPD  10 D may coordinate the delivery of cardiac stimulation therapy, including the termination of ATP and the initiation of the delivery of post-shock pacing, with the application of an anti-tachyarrhythmia shock merely through the detection of defibrillation pulses and without the need to communicate with the defibrillation device applying the anti-tachyarrhythmia shock. 
     In other examples, IPD  10 D and ICD  10 C may engage in communication to facilitate the appropriate detection of arrhythmias and/or delivery of therapy. The communication may include one-way communication in which one device is configured to transmit communication messages and the other device is configured to receive those messages. The communication may instead include two-way communication in which each device is configured to transmit and receive communication messages. Two-way communication and coordination of the delivery of patient therapies between IPD  10 D and ICD  10 C is described in commonly-assigned U.S. patent application Ser. No. 13/756,085, titled, “SYSTEMS AND METHODS FOR LEADLESS PACING AND SHOCK THERAPY,” filed Jan. 31, 2013, the entire content of which is incorporated by reference herein. 
     External device  30 C may be configured substantially similarly to external device  30 A described above with respect to  FIG. 1 . External device  30 C may be configured to communicate with one or both of ICD  10 C and IPD  10 D. In examples where external device  30 C only communicates with one of ICD  10 C and IPD  10 D, the non-communicative device may receive instructions from or transmit data to the device in communication with external device  30 C. In some examples, a user may interact with device  30 C remotely via a networked computing device. The user may interact with external device  30 C to communicate with IPD  10 D and/or ICD  10 C. 
     For example, the user may interact with external device  30 C to send an interrogation request and retrieve sensed physiological data or therapy delivery data stored by one or both of ICD  10 C and IPD  10 D, and program or update therapy parameters that define therapy, or perform any other activities with respect to ICD  10 C and IPD  10 D. Although the user is a physician, technician, surgeon, electrophysiologist, or other healthcare professional, the user may be patient  14 C in some examples. For example, external device  30 C may allow a user to program any coefficients, weighting factors, or techniques for determining difference metrics, scores, and/or thresholds, or other data described herein as being used by a medical device system to determine patient functional status based on accelerometer-generated data. As another example, external device  30 C may be used to program commands or operating parameters into ICD  10 C for controlling its functioning. External device  30 C may be used to interrogate ICD  10 C to retrieve data, including device operational data as well as physiological data accumulated in IMB memory, such as data associated with a patient-specific functional status parameter associated with a Sit-To-Stand test. ICD  10 C may be configured to implement the various features or aspects of the present disclosure for determining patient functional status based on accelerometer-generated data. 
     Medical device system  10 D is an example of a medical device system configured for determining patient functional status based on accelerometer-generated data. Such techniques as contemplated may be performed by processing circuitry of medical device system  10 D, such as processing circuitry of one or both of system  10 D and external device  30 C, individually, or collectively, as discussed in further detail below following a description provided in connection with  FIG. 33 . Other example medical device systems that may be configured to implement the techniques are described below. 
       FIG. 5  is a conceptual drawing illustrating another example medical device system  8 D that includes an extracardiovascular ICD system  100 B and IPD  10 D implanted within a patient. Medical device system  8 B may be configured to perform any of the techniques described herein with respect to medical device system  8 C of  FIGS. 4A-4C . Components with like numbers in  FIGS. 4A-4C  and  FIG. 5  may be similarly configured and provide similar functionality. 
     In the example of  FIG. 5 , extracardiovascular ICD system  100 B includes ICD  10 C coupled to a defibrillation lead  102 B. Unlike defibrillation lead  102 A of  FIGS. 4A-4C , defibrillation lead  102 B extends subcutaneously above the ribcage from ICD  10 C. In the illustrated example, defibrillation lead  102 B extends toward a center of the torso of patient  14 D, bends or turns near the center of the torso, and extends subcutaneously superior above the ribcage and/or sternum  110 . Defibrillation lead  102 B may be offset laterally to the left or the right of sternum  110  or located over sternum  110 . Defibrillation lead  102 B may extend substantially parallel to sternum  102  or be angled lateral from the sternum at either the proximal or distal end. 
     Defibrillation lead  102 B includes an insulative lead body having a proximal end that includes a connector  104  configured to be connected to ICD  10 C and a distal portion that includes one or more electrodes. Defibrillation lead  102 B also includes one or more conductors that form an electrically conductive path within the lead body and interconnect the electrical connector and respective ones of the electrodes. In the illustrated example, defibrillation lead  102 B includes a single defibrillation electrode  106  toward the distal portion of defibrillation lead  102 B, e.g., toward the portion of defibrillation lead  102 B extending along sternum  110 . Defibrillation lead  102 B is placed along sternum  110  such that a therapy vector between defibrillation electrode  106  and a housing electrode formed by or on ICD  10 C (or other second electrode of the therapy vector) is substantially across a ventricle of heart  16 D. 
     Defibrillation lead  102 B may also include one or more sensing electrodes, such as sensing electrodes  108 A and  108 B, located along the distal portion of defibrillation lead  102 B. In the example illustrated in  FIG. 5 , sensing electrodes  108 A and  108 B are separated from one another by defibrillation electrode  106 . In other examples, however, sensing electrodes  108 A and  108 B may be both distal of defibrillation electrode  106  or both proximal of defibrillation electrode  106 . In other examples, lead  102 B may include more or fewer electrodes at various locations proximal and/or distal to defibrillation electrode  106 , and lead  102 B may include multiple defibrillation electrodes, e.g., defibrillation electrodes  106 A and  106 B as illustrated in the example of  FIGS. 4A-4C . 
     Medical device system  8 D is an example of a medical device system configured for determining patient functional status based on accelerometer-generated data. Such techniques as contemplated may be performed by processing circuitry of medical device system  8 D, such as processing circuitry of one or both of system  8 D and external device  30 D, individually, or collectively, as discussed in further detail below. 
       FIG. 6  is a conceptual drawing illustrating an example configuration of IPD  10 D. As shown in  FIG. 6 , IPD  10 D includes case  130 , cap  138 , electrode  140 , electrode  132 , fixation mechanisms  142 , flange  134 , and opening  136 . Together, case  130  and cap  138  may be considered the housing of IPD  10 D. In this manner, case  130  and cap  138  may enclose and protect the various electrical components, e.g., circuitry, within IPD  10 D. Case  130  may enclose substantially all of the electrical components, and cap  138  may seal case  130  and create the hermetically sealed housing of IPD  10 D. Although IPD  10 D is generally described as including one or more electrodes, IPD  10 D may typically include at least two electrodes (e.g., electrodes  132  and  140 ) to deliver an electrical signal (e.g., therapy such as cardiac pacing) and/or provide at least one sensing vector. 
     Electrodes  132  and  140  are carried on the housing created by case  130  and cap  138 . In this manner, electrodes  132  and  140  may be considered leadless electrodes. In the example of  FIG. 6 , electrode  140  is disposed on the exterior surface of cap  138 . Electrode  140  may be a circular electrode positioned to contact cardiac tissue upon implantation. Electrode  132  may be a ring or cylindrical electrode disposed on the exterior surface of case  130 . Both case  130  and cap  138  may be electrically insulating. 
     Electrode  140  may be used as a cathode and electrode  132  may be used as an anode, or vice versa, for delivering cardiac pacing such as bradycardia pacing, CRT, ATP, or post-shock pacing. However, electrodes  132  and  140  may be used in any stimulation configuration. In addition, electrodes  132  and  140  may be used to detect intrinsic electrical signals from cardiac muscle. 
     Fixation mechanisms  142  may attach IPD  10 D to cardiac tissue. Fixation mechanisms  142  may be active fixation tines, screws, clamps, adhesive members, or any other mechanisms for attaching a device to tissue. As shown in the example of  FIG. 6 , fixation mechanisms  142  may be constructed of a memory material, such as a shape memory alloy (e.g., nickel titanium), that retains a preformed shape. During implantation, fixation mechanisms  142  may be flexed forward to pierce tissue and allowed to flex back towards case  130 . In this manner, fixation mechanisms  142  may be embedded within the target tissue. 
     Flange  144  may be provided on one end of case  130  to enable tethering or extraction of IPD  10 D. For example, a suture or other device may be inserted around flange  144  and/or through opening  146  and attached to tissue. In this manner, flange  144  may provide a secondary attachment structure to tether or retain IPD  10 D within heart  16 C (or  16 D) if fixation mechanisms  142  fail. Flange  144  and/or opening  146  may also be used to extract IPD  10 D once the IPD needs to be explanted (or removed) from patient  14 D if such action is deemed necessary. 
     Referring back to  FIGS. 4A-5 , medical device systems  8 C and  8 D are examples of medical device systems configured for determining patient functional status based on accelerometer-generated data. Such techniques may be performed by processing circuitry of medical device system  8 C or  8 D, such as processing circuitry of one or more of ICD  10 C, IPD  10 D, and external device  30 C or  30 D, individually, or collectively. Although the example medical devices systems  8 C and  8 D of  FIGS. 4A-5  are illustrated as including both ICD  10 C and IPD  10 D, other examples may include only one of ICD  10 C or IPD  10 D, alone, or in combination with other implanted or external devices. 
       FIG. 7  is a functional block diagram illustrating an example configuration of an IMD  10 . IMD  10  may correspond to any of ICD  10 A, ICM  10 B, ICD  10 C, IPD  10 D, or another IMB configured to implement the techniques for determining patient functional status based on accelerometer-generated data described in this disclosure. In the illustrated example, IMB  10  includes processing circuitry  160  and an associated memory  170 , sensing circuitry  162 , therapy delivery circuitry  164 , one or more sensors  166 , and communication circuitry  168 . However, ICD  10 A, ICM  10 B, ICD  10 C, and IPD  10 D need not include all of these components, or may include additional components. For example, ICM  10 B may not include therapy delivery circuitry  164 , in some examples (illustrated by intermittent line). 
     Memory  170  includes computer-readable instructions that, when executed by processing circuitry  160 , cause IMD  10  and processing circuitry  160  to perform various functions attributed to IMD  10  and processing circuitry  160  herein (e.g., calculate a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal). Memory  170  may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital or analog media. 
     Processing circuitry  160  may include fixed function circuitry and/or programmable processing circuitry. Processing circuitry  160  may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry. In some examples, processing circuitry  160  may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry. The functions attributed to processing circuitry  160  herein may be embodied as software, firmware, hardware or any combination thereof. 
     Sensing circuitry  162  and therapy delivery circuitry  164  are coupled to electrodes  190 . Electrodes  190  illustrated in  FIG. 7  may correspond to, for example: electrodes  12 ,  22 ,  24 ,  26 ,  28 ,  44 , and  44  of ICD  10 A ( FIG. 1 ); electrodes  64  and  66  of ICM  10 B ( FIG. 3 ); electrodes  106 ,  108 , and one or more housing electrodes of ICD  10 C ( FIGS. 4A-5 ); or electrodes  132  and  140  of IPD  10 D ( FIG. 6 ). 
     Sensing circuitry  162  monitors signals from a selected two or more of electrodes  190  in order to monitor electrical activity of heart  26 , impedance, or other electrical phenomenon. Sensing of a cardiac electrical signal may be done to determine heart rates or heart rate variability, or to detect arrhythmias (e.g., tachyarrhythmias or bradycardia) or other electrical signals. In some examples, sensing circuitry  162  may include one or more filters and amplifiers for filtering and amplifying a signal received from electrodes  190 . 
     The resulting cardiac electrical signal may be passed to cardiac event detection circuitry that detects a cardiac event when the cardiac electrical signal crosses a sensing threshold. The cardiac event detection circuitry may include a rectifier, filter and/or amplifier, a sense amplifier, comparator, and/or analog-to-digital converter. Sensing circuitry  162  outputs an indication to processing circuitry  160  in response to sensing of a cardiac event (e.g., detected P-waves or R-waves). 
     In this manner, processing circuitry  160  may receive detected cardiac event signals corresponding to the occurrence of detected R-waves and P-waves in the respective chambers of heart  26 . Indications of detected R-waves and P-waves may be used for detecting ventricular and/or atrial tachyarrhythmia episodes, e.g., ventricular or atrial fibrillation episodes. Some detection channels may be configured to detect cardiac events, such as P- or R-waves, and provide indications of the occurrences of such events to processing circuitry  160 , e.g., as described in U.S. Pat. No. 5,117,824 to Keimel et al., which issued on Jun. 2, 1992 and is entitled, “APPARATUS FOR MONITORING ELECTRICAL PHYSIOLOGIC SIGNALS,” and is incorporated herein by reference in its entirety. 
     Sensing circuitry  162  may also include a switch module to select which of the available electrodes  190  (or electrode polarities) are used to sense the heart activity. In examples with several electrodes  190 , processing circuitry  160  may select the electrodes that function as sense electrodes, i.e., select the sensing configuration, via the switch module within sensing circuitry  162 . Sensing circuitry  162  may also pass one or more digitized EGM signals to processing circuitry  160  for analysis, e.g., for use in cardiac rhythm discrimination. 
     Processing circuitry  160  may implement programmable counters. If IMD  10  is configured to generate and deliver pacing pulses to heart  26 , such counters may control the basic time intervals associated with bradycardia pacing (e.g., DDD, VVI, DVI, VDD, AAI, DDI, DDDR, VVIR, DVIR, VDDR, AAIR, DDIR pacing) and other modes of pacing. Intervals defined by processing circuitry  160  may include atrial and ventricular pacing escape intervals, refractory periods during which sensed P-waves and R-waves are ineffective to restart timing of the escape intervals, and the pulse widths of the pacing pulses. The durations of these intervals may be determined by processing circuitry  160  in response to pacing mode parameters stored in memory  170 . 
     Interval counters implemented by processing circuitry  160  may be reset upon sensing of R-waves and P-waves with detection channels of sensing circuitry  162 , or upon the generation of pacing pulses by therapy delivery circuitry  164 , and thereby control the basic timing of cardiac pacing functions, including bradycardia pacing, CRT, ATP, or post-shock pacing. The value of the count present in the interval counters when reset by sensed R-waves and P-waves may be used by processing circuitry  160  to measure the durations of R-R intervals, P-P intervals, P-R intervals and R-P intervals, which are measurements that may be stored in memory  170 . Processing circuitry  160  may use the count in the interval counters to detect a tachyarrhythmia event, such as atrial fibrillation (AF), atrial tachycardia (AT), VF, or VT. These intervals may also be used to detect the overall heart rate, ventricular contraction rate, and heart rate variability. A portion of memory  170  may be configured as a plurality of recirculating buffers, capable of holding series of measured intervals, which may be analyzed by processing circuitry  160  in response to the occurrence of a pace or sense interrupt to determine whether the patient&#39;s heart  26  is presently exhibiting atrial or ventricular tachyarrhythmia. 
     In some examples, an arrhythmia detection method may include any suitable tachyarrhythmia detection algorithms. In one example, processing circuitry  160  may utilize all or a subset of the rule-based detection methods described in U.S. Pat. No. 5,545,186 to Olson et al., entitled, “PRIORITIZED RULE BASED METHOD AND APPARATUS FOR DIAGNOSIS AND TREATMENT OF ARRHYTHMIAS,” which issued on Aug. 13, 1996, or in U.S. Pat. No. 5,755,736 to Gillberg et al., entitled, “PRIORITIZED RULE BASED METHOD AND APPARATUS FOR DIAGNOSIS AND TREATMENT OF ARRHYTHMIAS,” which issued on May 26, 1998. U.S. Pat. No. 5,545,186 to Olson et al. U.S. Pat. No. 5,755,736 to Gillberg et al. is incorporated herein by reference in their entireties. However, other arrhythmia detection methodologies, such as those methodologies that utilize timing and morphology of the electrocardiogram, may also be employed by processing circuitry  160  in other examples. 
     In some examples, processing circuitry  160  may determine that tachyarrhythmia has occurred by identification of shortened R-R (or P-P) interval lengths. Generally, processing circuitry  160  detects tachycardia when the interval length falls below 220 milliseconds and fibrillation when the interval length falls below 180 milliseconds. In other examples, processing circuitry  160  may detect ventricular tachycardia when the interval length falls between 330 milliseconds and ventricular fibrillation when the interval length falls below 240 milliseconds. These interval lengths are merely examples, and a user may define the interval lengths as desired, which may then be stored within memory  170 . This interval length may need to be detected for a certain number of consecutive cycles, for a certain percentage of cycles within a running window, or a running average for a certain number of cardiac cycles, as examples. In other examples, additional patient parameters may be used to detect an arrhythmia. For example, processing circuitry  160  may analyze one or more morphology measurements, impedances, or any other physiological measurements to determine that patient  14  is experiencing a tachyarrhythmia. 
     In addition to detecting and identifying specific types of cardiac events, e.g., cardiac depolarizations, sensing circuitry  162  may also sample the detected intrinsic signals to generate an electrogram or other time-based indication of cardiac events. Sensing circuitry  162  may include an analog-to-digital converter or other circuitry configured to sample and digitize the electrical signal sensed via electrodes  190 . Processing circuitry  160  may analyze the digitized signal for a variety of purposes, including morphological identification or confirmation of tachyarrhythmia of heart  26 . As another example, processing circuitry  160  may analyze the digitized cardiac electrogram signal to identify and measure a variety of morphological features of the signal. 
     In some examples, sensing circuitry  162  is configured to sense other physiological signals of patient. For example, sensing circuitry  162  may be configured to sense signals that vary with changing thoracic impedance of patient  14 . The thoracic impedance may vary based on fluid volume or edema in patient  14 . 
     Sensing circuitry  162  may use any two or more of electrodes  190  to sense thoracic impedance. As the tissues within the thoracic cavity of patient  14  change in fluid content, the impedance between two electrodes may also change. For example, the impedance between a defibrillation coil electrode ( 42 ,  44 ,  106 ) and the housing electrode may be used to monitor changing thoracic impedance. 
     In some examples, processing circuitry  160  measured thoracic impedance values to determine a fluid index. As more fluid is retained within patient  14 , e.g., edema increases, and the thoracic impedance decreases or remains relatively high, the fluid index increases. Conversely, as the thoracic impedance increases or remains relatively low, the fluid index decreases. An example system for measuring thoracic impedance and determining a fluid index is described in U.S. Patent Publication No. 2010/0030292 to Sarkar et al., entitled, “DETECTING WORSENING HEART FAILURE BASED ON IMPEDANCE MEASUREMENTS,” which published on Feb. 4, 2010 and is incorporated herein by reference in its entirety. 
     The thoracic impedance may also vary with patient respiration. In some examples, processing circuitry  160  may determine values of one or more respiration-related patient parameters based on thoracic impedance sensed by sensing circuitry  162 . Respiration-related patient parameters may include, as examples, respiration rate, respiration depth, or the occurrence or magnitude of dyspnea or apneas. 
     The magnitude of the cardiac electrogram may also vary based on patient respiration, e.g., generally at a lower frequency than the cardiac cycle. In some examples, processing circuitry  160  and/or sensing circuitry  162  may filter the cardiac electrogram to emphasize the respiration component of the signal. Processing circuitry  160  may analyze the filtered cardiac electrogram signal to determine values of respiration-related patient parameters. 
     In the example of  FIG. 7 , IMB  10  includes one or more sensors  166  coupled to sensing circuitry  162 . Although illustrated in  FIG. 7  as included within IMB  10 , one or more of sensors  166  may be external to IMB  10 , e.g., coupled to IMB  10  via one or more leads, or configured to wirelessly communicate with IMB  10 . In some examples, sensors  166  transduce a signal indicative of a patient parameter, which may be amplified, filtered, or otherwise processed by sensing circuitry  162 . In such examples, processing circuitry  160  determines values of patient parameters based on the signals. In some examples, sensors  166  determine the patient parameter values, and communicate them, e.g., via a wired or wireless connection, to processing circuitry  160 . 
     In some examples, sensors  166  include one or more accelerometers  167 , e.g., one or more 3-axis accelerometers. Signals generated by the one or more accelerometers  167 , such as one or more of a sagittal axis signal, a vertical axis signal and a transverse axis signal, may be indicative of, as examples, gross body movement (e.g., activity) of patient  14 , patient posture, heart sounds or other vibrations or movement associated with the beating of the heart, or coughing, rales, or other respiration abnormalities. In some examples, sensors  166  include one or more microphones configured to detect heart sounds or respiration abnormalities, and/or other sensors configured to detect patient activity or posture, such as gyroscopes and/or strain gauges. In some examples, sensors  166  may include sensors configured to transduce signals indicative of blood flow, oxygen saturation of blood, or patient temperature, and processing circuitry  160  may determine patient parameters values based on these signals. 
     In some examples, sensors  166  include one or more pressure sensors that transduce one or more signals indicative of blood pressure, and processing circuitry  160  determines one or more patient parameter values based on the pressure signals. Patient parameter values determined based on pressure may include, as examples, systolic or diastolic pressure values, such as pulmonary artery diastolic pressure values. In some examples, a separate pressure-sensing IMD  50  includes one or more sensors and sensing circuitry configured to generate a pressure signal, and processing circuity  160  determines patient parameter values related to blood pressure based on information received from IMD  50 . 
     Therapy delivery circuitry  164  is configured to generate and deliver electrical therapy to the heart. Therapy delivery circuitry  164  may include one or more pulse generators, capacitors, and/or other components capable of generating and/or storing energy to deliver as pacing therapy, defibrillation therapy, cardioversion therapy, other therapy or a combination of therapies. In some instances, therapy delivery circuitry  164  may include a first set of components configured to provide pacing therapy and a second set of components configured to provide anti-tachyarrhythmia shock therapy. In other instances, therapy delivery circuitry  164  may utilize the same set of components to provide both pacing and anti-tachyarrhythmia shock therapy. In still other instances, therapy delivery circuitry  164  may share some of the pacing and shock therapy components while using other components solely for pacing or shock delivery. 
     Therapy delivery circuitry  164  may include charging circuitry, one or more charge storage devices, such as one or more capacitors, and switching circuitry that controls when the capacitor(s) are discharged to electrodes  190  and the widths of pulses. Charging of capacitors to a programmed pulse amplitude and discharging of the capacitors for a programmed pulse width may be performed by therapy delivery circuitry  164  according to control signals received from processing circuitry  160 , which are provided by processing circuitry  160  according to parameters stored in memory  170 . Processing circuitry  160  controls therapy delivery circuitry  164  to deliver the generated therapy to the heart via one or more combinations of electrodes  190 , e.g., according to parameters stored in memory  170 . Therapy delivery circuitry  164  may include switch circuitry to select which of the available electrodes  190  are used to deliver the therapy, e.g., as controlled by processing circuitry  160 . 
     In some examples, processing circuitry  160  periodically, i.e., for each of a plurality of periods, determines a respective value for each of a plurality of patient parameters. The determined patient parameter values are stored as patient parameter values  174  in memory  170 . In some examples, the length of each period is greater than one hour, such as a predetermined integer number of hours or days. In some examples, the period length is between eight hours and three days, such as one day. 
     Each of patient parameter values  174  may be the single value of a patient parameter determined during the period. In other examples, each of patient parameter values  174  is a representative value determined based on a plurality of values determined during the period. In some examples, patient parameter values  174  may include one or more means, medians, modes, sums, or other values determined based on a plurality of values of a patient parameter determined during the period. 
     The plurality of patient parameters may include one or more parameters determined based on the cardiac electrogram, such as one or more heart rate parameters, and/or one or more tachyarrhythmia episode parameters. Example heart rate parameters include average heart rate during the period, average daytime heart rate during the period, average nighttime heartrate during the period, and one or more measures of heart rate variability during the period. Example tachyarrhythmia episode parameters include the number, frequency and/or duration (total, mean, or median) of tachyarrhythmia episodes during the period, such as atrial tachycardia episodes, atrial fibrillation episodes, or non-sustained tachyarrhythmia (NST) episodes. NST episodes may be a series of short R-R intervals greater than an NST threshold number of short R-R intervals, but fewer than a number of intervals to detect (NID) for ventricular tachyarrhythmia. Another example patient parameter that processing circuitry  160  may determine based on the cardiac electrogram is the ventricular rate during atrial tachyarrhythmia, e.g., atrial fibrillation, which may be a mean or median value during the period. 
     Other patient parameters determined based on the cardiac electrogram include morphological features of the cardiac electrogram, such as QRS width or duration, QT interval length, T-wave amplitude, R-R interval length, an interval between a peak and the end of the T-wave, a ratio between the T-wave peak to end interval and the QT interval lengths, or T-wave alternan. The presence of T-wave alternan may be detected as a periodic (e.g., beat-to-beat) variation in the amplitude or morphology of the T-wave. A T-wave alternan patient parameter value  174  may be an indication of the presence, number, frequency, or duration (total, mean, or median) of T-wave alternan episodes. Other patient parameter values  174  based cardiac electrogram morphological interval lengths may be means or medians of a plurality of measurements made during the period, e.g., daily mean or median values. 
     The plurality of patient parameters may additionally or alternatively include at least one patient parameter indicative of edema, and processing circuitry  160  may determine values  174  of such patient parameters based on sensed thoracic impedance, as described above. In some examples, a patient parameter value  174  may be a maximum, minimum, mean, or median thoracic impedance value during a period. In some examples, a patient parameter value  174  may be a fluid index value during the period. Processing circuitry  160  may increment and decrement a fluid index value based on an accumulation of differences between a thoracic impedance value (or short-term average of impedance values) and a threshold determined based on a long-term average of thoracic impedance values. 
     The plurality of patient parameters may additionally or alternatively include at least one patient parameter indicative of patient activity, e.g., gross patient body movement or motion. In some examples, processing circuitry  160  determines a number of activity counts based on one or more accelerometer signals crossing exceeding one or more thresholds. A patient parameter value  174  during a period may be a total, mean, or median number of counts during the period. 
     The plurality of patient parameters may additionally or alternatively include at least one patient parameter indicative of cardiovascular pressure, and processing circuitry  160  may determine values  174  of such patient parameters based on generated pressure waveform, e.g., generated by a sensor  166  or pressure-sensing IMB  50 , as described above. The patient parameter values  174  for the period may include a maximum, minimum, mean, or median of systolic pressure and/or diastolic pressure, e.g., pulmonary artery diastolic pressure. 
     The plurality of patient parameters may additionally or alternatively include at least one patient parameter determined based on patient respiration, and processing circuitry  160  may determine values  174  of such parameters based on a generated signal that varies based on respiration as described above, such as a signal that varies based on thoracic impedance. The patient parameter values  174  for the period may include a maximum, minimum, mean, or median of respiration rate, e.g., for a day, daytime, or nighttime. The patient parameter values  174  for the period may include an indication of the presence, a number, a frequency, or a duration (total, mean, or median) of respiration episodes, such as apneas or dyspneas. 
     Processing circuitry  160  may additionally or alternatively determine values  174  of one or more patient parameters based on a generated signal that varies based on sound or other vibrations, which may indicate heart sounds, coughing, or rales. Patient parameter values may include morphological measurements of the S1 and S2 heart sounds, the presence or frequency of occurrence of S3 and/or S4 heart sounds, or the presence, number, frequency, or duration (total, mean or media) of episodes or coughing or rales. Other patient parameter values  174  that processing circuitry  160  may additionally or alternatively periodically determine based on signals generated by sensors  166  include maximum, minimum, mean, or median values of blood flow, blood oxygen saturation, or temperature. 
     The plurality of patient parameters may additionally or alternatively include at least one patient parameter determined based on delivery of therapy to patient  14 , e.g., by IMB  10 . In some examples, a patient parameter value  174  for a period indicates an amount of cardiac pacing delivered to the patient during the period, such as a total duration or percentage of the period during which atrial pacing, ventricular pacing, and/or CRT was delivered. 
     In some examples, the plurality of patient parameter values  174  determined for each period includes: a percentage of the period during which IMB  10  delivered ventricular pacing to patient  14 ; a percentage of the period during which IMB  10  delivered atrial pacing to patient  14 ; an average daytime ventricular heart rate; an average nighttime ventricular heart rate; a frequency or duration of atrial tachycardia event, atrial fibrillation events, and/or NSTs during the period; a total number of patient activity counts during the period; a measure of heart rate variability during the period; a daily thoracic impedance value; and a fluid index value. In some examples, the plurality of patient parameter values  174  includes all or subset of the parameters included in Cardiac Compass® trends generated by IMDs available from Medtronic, plc, of Dublin Ireland. In some examples, the plurality of patient parameter values  174  additionally includes one or more cardiac electrogram morphology parameters. 
     The plurality of patient parameters may additionally or alternatively include at least one patient parameter determined or derived based on the shape or form of at least one of a sagittal axis signal, a vertical axis signal and a transverse axis signal as produced, generated or provided by the accelerometer(s)  166 . In some examples, a patient parameter value(s)  174  is a quantified score associated with a Sit-To-Stand test. In some examples, a patient parameter value(s)  174  is at least one of: a rate of change metric(s) associated with a Sit-To-Stand test; a definite integral metric(s) associated with a Sit-To-Stand test; a length of time metric(s) associated with a Sit-To-Stand test; a peak amplitude metric(s) associated with a Sit-To-Stand test; a peak-peak amplitude metric(s) associated with a Sit-To-Stand test; an averaged patient-specific functional status parameter associated with a Sit-To-Stand test; a time occurrence of at least one inflection point associated with a Sit-To-Stand test; a symmetry characteristic(s) or metric(s) associated with a Sit-To-Stand test; a velocity metric(s) associated with a Sit-To-Stand test; a distance metric(s) associated with a Sit-To-Stand test; a kinetic energy metric(s) associated with a Sit-To-Stand test; a potential energy metric(s) associated with a Sit-To-Stand test; a derivative metric(s) associated with a Sit-To-Stand test; a metric(s) to distinguish a sit-to-stand movement from a stand-to-sit movement; identify a metric(s) to a stand-to-sit morphology; a metric(s) to identify sit-to-stand morphology. Still other examples are possible. 
     Processing circuitry  160  determines a difference metric  176  for each of the plurality patient parameters for the period. Processing circuitry  160  determines the difference metric  176  for each patient parameter based on a difference between a current value  174  of the patient parameter for the current period, and an immediately preceding value  174  of the patient parameter for the immediately preceding period. In some examples, processing circuitry  160  determines the difference metric  176  for each of the patient parameters according to the following equation: 
         V   t,param     n   =Value t−1 −Value t−2    (Eq. 1)
 
     In some examples, processing circuitry  160  determines the difference metric  176  for each of the plurality patient parameters for the period based on the difference between the current and preceding values, and a standard deviation (or other measure of variation) of values  174  of the patient parameter for N preceding periods. N is an integer constant, e.g., between 5 and 50, such as between 7 and 15 or, in one example, 15. In examples in which each period is a day, the N preceding periods may be N preceding days. Determining the difference metric based on the difference between the current and preceding values and a standard deviation or other measure of variation allow the difference metric to better represent the difference in the patient parameter during the current period rather than baseline variation of the patient parameter and/or noise. In some examples, processing circuitry  160  determines the difference metric  176  for each of the patient parameters according to the following equation: 
     
       
         
           
             
               
                 
                   
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                         Value 
                         
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     Processing circuitry  160  determines a score  178  for the period based on the plurality of patient parameter-specific difference metrics  176  for the period. In some examples, processing circuitry  160  determines the score  178  for the period based on a sum of squares of the difference metrics  176  for the period or a sum of absolute values of the difference metrics  176 . The difference metrics  176  may be positive or negative, and use of the sum of squares or absolute values may enable the score  178  to reflect the absolute magnitudes of change of the plurality of patient parameters during the period. In some examples, processing circuitry  160  determines the score  178  for the period using a sum of squares of difference metrics  176  according to the following equation, where n is the number of patient parameters for which difference metrics  176  are determined during the period (in this case 8): 
       score t =Σ n=1   8 V t,param     n     2    (Eq. 3)
 
     In some examples, processing circuitry  160  applies coefficients or weights to one or more of difference metrics  176  when determining a score  178  for a period. The weights may be determined and/or adjusted empirically based on an analysis of the sensitivity and specificity of the score  178  for determining patient functional status based on accelerometer-generated data. The values of the weights may be adjusted over time, e.g., on a period-by-period or less frequent basis. 
     Processing circuitry  160  also determines a threshold  180  for the period based on scores  178  for N preceding periods, wherein N is the integer constant, e.g., 15. In some examples, processing circuitry  160  determines the threshold  180  based on a mean or median of the N preceding scores, e.g., by multiplying a median of the N scores and a coefficient. The coefficient may be, for example, between 1 and 3, and determined for a given patient  14  or patient population based on a receiver operator characteristic (ROC). 
     Communication circuitry  168  includes any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as an external device  30  or another IMD or sensor. Under the control of processing circuitry  160 , communication circuitry  168  may receive downlink telemetry from and send uplink telemetry to external device  30  or another device with the aid of an antenna, which may be internal and/or external. In some examples, communication circuitry  168  may communicate with a local external device, and processing circuitry  160  may communicate with a networked computing device via the local external device and a computer network, such as the Medtronic CareLink® Network developed by Medtronic, plc, of Dublin, Ireland. 
     A clinician or other user may retrieve data from IMB  10  using external device  30  or another local or networked computing device configured to communicate with processing circuitry  160  via communication circuitry  168 . The clinician may also program parameters of IMB  10  using external device  30  or another local or networked computing device. In some examples, the clinician may select patient parameters used to quantify patient-specific functional status associated with a Sit-To-Stand test. In general, such parameters may include single data points (i.e., a single score that quantifies patient-specific functional status associated with a particular Sit-To-Stand test) or a sequence of data points that may be plotted as a trend over time. 
       FIG. 8  is a functional block diagram illustrating an example configuration of an external device  30  configured to communicate with one or more IMDs  10 . In the example of  FIG. 8 , external device  30  includes processing circuitry  200 , memory  202 , user interface (UI)  204 , and communication circuitry  206 . External device  30  may correspond to any of external devices  30 A- 30 C described with respect to  FIGS. 1, 2, and 4A-5 . External device  30  may be a dedicated hardware device with dedicated software for the programming and/or interrogation of an IMB  10 . Alternatively, external device  30  may be an off-the-shelf computing device, e.g., a smartphone running a mobile application that enables external device  30  to program and/or interrogate IMD  10 . 
     In some examples, a user uses external device  30  to select or program any of the values for operational parameters of IMB  10 , e.g., for measuring or determining patient functional status based on accelerometer-generated data. In some examples, a user uses external device  30  to receive data collected by IMD  10 , such as patient parameter values  174  or other operational and performance data of IMD  10 . The user may also receive alerts provided by IMD  10  that indicate that an acute cardiac event, e.g., ventricular tachyarrhythmia, is predicted. The user may interact with external device  30  via UI  204 , which may include a display to present a graphical user interface to a user, and a keypad or another mechanism (such as a touch sensitive screen) for receiving input from a user. External device  30  may communicate wirelessly with IMB  10  using communication circuitry  206 , which may be configured for RF communication with communication circuitry  168  of IMD  10 . 
     Processing circuitry  200  may include any combination of integrated circuitry, discrete logic circuity, analog circuitry, such as one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs). In some examples, processing circuitry  200  may include multiple components, such as any combination of one or more microprocessors, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry, and/or analog circuitry. 
     Memory  202  may store program instructions, which may include one or more program modules, which are executable by processing circuitry  200 . When executed by processing circuitry  200 , such program instructions may cause processing circuitry  200  and external device  30  to provide the functionality ascribed to them herein. The program instructions may be embodied in software, firmware and/or RAMware. Memory  202  may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media. 
     In some examples, processing circuitry  200  of external device  30  may be configured to provide some or all of the functionality ascribed to processing circuitry  160  of IMD  10  herein. For example, processing circuitry  200  may receive physiological signals generated by one or more IMDs  10  and determine values  174  of each of a plurality of patient parameters during each of a plurality of periods, and/or may receive patient parameter values  174  for the plurality of periods from one or more IMDs  10 . Processing circuitry  200  may determine metrics  176 , scores  178 , and thresholds  180  based on the patient parameter values  174  in the manner described above with respect to processing circuitry  160  of IMB  10  for determining patient functional status based on accelerometer-generated data. 
       FIG. 9  is a functional block diagram illustrating an example system that includes external computing devices, such as a server  224  and one or more other computing devices  230 A- 230 N, that are coupled to IMD  10  and external device  30  via a network  222 . In this example, IMD  10  may use its communication module  168  to, e.g., at different times and/or in different locations or settings, communicate with external device  30  via a first wireless connection, and to communication with an access point  220  via a second wireless connection. In the example of  FIG. 9 , access point  220 , external device  30 , server  224 , and computing devices  230 A- 230 N are interconnected, and able to communicate with each other, through network  222 . 
     Access point  220  may comprise a device that connects to network  222  via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point  220  may be coupled to network  222  through different forms of connections, including wired or wireless connections. In some examples, access point  220  may be co-located with patient  14 . Access point  220  may interrogate IMB  10 , e.g., periodically or in response to a command from patient  14  or network  222 , to retrieve physiological signals, patient parameter values  174 , difference metrics  176 , scores  178 , thresholds  180 , alerts of acute cardiac events, and/or other operational or patient data from IMD  10 . Access point  220  may provide the retrieved data to server  224  via network  222 . 
     In some cases, server  224  may be configured to provide a secure storage site for data that has been collected from IMD  10  and/or external device  30 . In some cases, server  224  may assemble data in web pages or other documents for viewing by trained professionals, such as clinicians, via computing devices  230 A- 230 N. The illustrated system of  FIG. 9  may be implemented, in some aspects, with general network technology and functionality similar to that provided by the Medtronic CareLink® Network developed by Medtronic plc, of Dublin, Ireland. 
     In some examples, one or more of access point  220 , server  224 , or computing devices  230  may be configured to perform, e.g., may include processing circuitry configured to perform, some or all of the techniques described herein, e.g., with respect to processing circuitry  160  of IMD  10  and processing circuitry  200  of external device  30 , relating to determining patient functional status based on accelerometer-generated data. In the example of  FIG. 9 , server  224  includes a memory  226  to store signals or patient parameter values  174  received from IMD  10  and/or external device  30 , and processing circuitry  228 , which may be configured to provide some or all of the functionality ascribed to processing circuitry  160  of IMD  10  and processing circuitry  200  of external device  30  herein. For example, processing circuitry  228  may determine values  174  of each of a plurality of patient parameters during each of a plurality of periods, and/or may receive patient parameter values  174  for the plurality of periods from one or more IMDs  10 . Processing circuitry  228  may determine metrics  176 , scores  178 , and thresholds  180  based on the patient parameter values  174  in the manner described above with respect to processing circuitry  160  of IMD  10  for determining patient functional status based on accelerometer-generated data. 
     As mentioned above, a medical device system according to certain features or aspects of this disclosure includes accelerometer circuitry configured to generate a number of signals including a sagittal (frontal) axis signal, as well as processing circuitry configured to calculate a patient-specific functional status parameter associated with a Sit-To-Stand test from the sagittal axis signal. Such an implementation may, among other things, provide an objective measure of change (or not) in well-being to help guide therapies, because a patient-specific functional status parameter associated with a Sit-To-Stand test can help determine whether health is improving, declining, or stable. 
       FIG. 10  is a flowchart illustrating a first example method  1000  for determining patient functional status based on accelerometer-generated data in accordance with the disclosure. Method  1000  may be implemented by any one of the implantable medical devices discussed above in connection with  FIGS. 1-9 , because each one of the same is configured to include at least one accelerometer (i.e., accelerometer circuity), as well as communication and processing circuitry (see  FIG. 7  and corresponding description) to facilitate determining patient functional status based on accelerometer-generated data. 
     For example, and with reference to ICM  10 B of  FIG. 2 , a command may be detected ( 1002 ) to activate or power-on an on-board accelerometer, and then a three-axis accelerometer signal measurement may be acquired ( 1004 ) over a finite interval of time. In other embodiments, the three-axis accelerometer measurement by be continuously monitored, and then a sit-to-stand transition may be identified based on the three-axis accelerometer. The three-axis accelerometer signal measurement may correspond to a sagittal axis signal, a vertical axis signal and a transverse axis signal each acquired over a common time window. Next, a patient-specific functional status parameter associated with a Sit-To-Stand test may be calculated from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal. 
     In practice, the patient-specific functional status parameter may be calculated by the ICM  10 B, then appended to a historical dataset of like status parameters, each uniquely identified by a timestamp and organized as a list, and then transmitted alone or together, with at least one status parameter in the historical dataset, to an external computing device or system for subsequent processing (branch  1006 ,  1010 ,  1012 ). The transmission may not necessarily be in response to a request however. For example, a transmission may occur automatically whenever a network connection becomes available, and/or at a particular time of day based upon a predefined schedule. Alternatively, raw data associated with each one of the sagittal axis signal, the vertical axis signal and the transverse axis signal acquired over the common time window may be stored, possibly in a modified form (e.g., compressed or encoded), and then transmitted alone or together with other like data to the at least one external computing device or system for subsequent processing (branch  1006 ,  1008 ,  1012 ). Such an implementation may be beneficial and/or advantageous in many respects. 
     For example, patient functional status can help determine whether health is improving, declining, or relatively steady. The 6-minute walk test (6MWT) is a standard for measuring patient-specific functional status. There may be a correlation in diagnostic benefit between the 6MWT and a Sit-To-Stand test (SST) for pulmonary disease patients that measures the time to perform multiple Sit-To-Stand-to-sit movements. The main component of a SST that takes the most effort for a patient typically is time-to-stand-up. Method  100  leverages at least the sagittal (frontal) axis of a 3D accelerometer to identify when a person stands up, and measurements are taken on the signal during the standing up period to assess patient functional status. As discussed in further detail below, a number of calculations may be made and then an SST measure may be produced that would provide clinical meaning. 
       FIG. 11  is a conceptual diagram  1100  illustrating a sagittal axis  1102 , a vertical axis  1104  and transverse axis  1106  in a three-dimensional coordinate system. 
       FIG. 12  is a plot  1200  illustrating a sagittal axis signal  1202 , a vertical axis signal  1204 , and transverse axis signal  1206  produced by an accelerometer (see e.g.,  FIG. 7 , element  166 ) during a series of sit-stand and stand-sit movements labeled A 1 -A 2 , B 1 -B 2  and C 1 -C 2 , respectively. The sagittal axis signal  1202  corresponds to the trace or trend that exhibits the largest amplitude variations primarily on the (+) side of the y-axis (arbitrary units) across each one of A 1 -A 2 , B 1 -B 2  and C 1 -C 2 . The vertical axis signal  1204  corresponds to the trace or trend that exhibits moderate amplitude variations on the (+) side and the (−) side of the y-axis across each one of A 1 -A 2 , B 1 -B 2  and C 1 -C 2 . The transverse axis signal  1206  corresponds to the trace or trend that exhibits amplitude variations primarily on the (−) side of the y-axis across each one of A 1 -A 2 , B 1 -B 2  and C 1 -C 2 , exhibits a number of zero-crossings that is less than a number of zero-crossings of the vertical axis signal  1204 . 
       FIG. 13  is a plot  1300  illustrating the slope of a segment of the sagittal axis signal  1202  of  FIG. 12 . More specifically, the slope of a segment corresponding to stand-up movement A 1  is illustrated together with a representation of the area-under-curve taken over a time interval corresponding to an entirety of the stand-up movement A 1 , collectively “S 1 , I 1 ” in  FIG. 13 . Further, the slope of a segment corresponding to stand-up movement B 1  is illustrated together with a representation of the area-under-curve taken over a time interval corresponding to an entirety of the stand-up movement B 1 , collectively “S 2 , I 2 ” in  FIG. 13 . Still further, the slope of a segment corresponding to stand-up movement C 1  is illustrated together with a representation of the area-under-curve taken over a time interval corresponding to an entirety of the stand-up movement C 1 , collectively “S 3 , I 3 ” in  FIG. 13 . As understood from the data, |S 1 |&gt;‥S 2 |&gt;|S 2 |, and |I 1 |&lt;|I 2 |&lt;|I 3 |, indicating presence of increasing patient fatigue over the duration of the series of sit-stand and stand-sit movements. Accordingly, it is contemplated that the sagittal axis signal  1202  can be leveraged for measuring or determining patient functional status, as part of a SST performance test for example. Other features of the sagittal axis signal  1202  indicate patient fatigue as well. 
       FIG. 14  is a conceptual diagram  1400  illustrating a change in several characteristic of the sagittal axis signal  1202  of  FIG. 12  over the series of sit-stand and stand-sit movements. More specifically, slope (rate of change) decreases over stand-up movements A 1 -C 1  (see  FIG. 12 ), area-under-curve (definite integral) increases over stand-up movements A 1 -C 1 , time to stand (T 1 -T 3 ) increases over stand-up movements A 1 -C 1 , and amplitude (J 1 -J 3 ) varies over stand-up movements A 1 -C 1 . Patient fatigue over the duration of the series of sit-stand and stand-sit movements is indicated due to such trends. Accordingly, it is contemplated that the sagittal axis signal  1202  can be leveraged for measuring or determining patient functional status, as part of a SST performance test for example. This is because 3D accelerometers in the ICM  10 B, for example, which is implanted in the chest, and are relatively stationary over the lifetime of the implant. The stationary chest location presents an opportunity to monitor changes in the upper body that occur during various activities. As a person gets in and out of a chair for example the upper body has a reproducible motion (similar to a “bowing” motion) that may be identified with signals produced by the accelerometers. Accordingly, an algorithm may be developed for determining patient functional status based on at least the sagittal axis signal  1202 . 
     For example,  FIG. 15  is a plot  1500  illustrating several characteristic of the sagittal axis signal of  FIG. 12 . And,  FIG. 16  is a flowchart illustrating a second example method  1600  for determining patient functional status based on accelerometer-generated data in accordance with the disclosure. The method  1600  of  FIG. 16  is also an example of how to detect sit-to-stand and stand-to-sit movements, and may be performed by any processing circuitry and/or IMD of any of medical device system described throughout. 
     With reference to  FIGS. 15-16 , an example algorithm may include: 
     Identify baseline ( FIG. 15  “baseline”;  FIG. 16  “loop  1602 ” where “GTOg=0” assigns a value of a current sample of the sagittal axis signal  1202  to value “0”) by:
         determining whether the current sample of the sagittal axis signal  1202  is within a certain number of units (e.g., 0.1 g) of baseline (e.g., 0 g), or another threshold different than baseline if determined applicable, for at least a certain number (e.g., 15) of seconds; as an alternative, since the waveform shape associated with sitting and the waveform shape associated with standing are similar, activity/steps before and after the shape may help differentiate the two, e.g., the waveform will have little to no variation prior to a standing movement and increased variation following completion of the standing movement, and an opposite effect will be visible in the sagittal axis signal over a stand-to-sit movement.       

     Identify start and end of standing up ( FIG. 15  “start/end”;  FIG. 16  “loop  1604 ”) by:
         determining whether the amplitude of the sagittal axis signal  1202  amplitude increases over a threshold (e.g. 0.2 g) and decreases to less than the baseline within a certain time period (e.g. within 0.5 s-5 s).       

     Determine standing up characteristics ( FIG. 16  “loop  1606 ” which searches for peak values in the signal and “element  1608 ”) by:
         analyzing the sagittal axis signal  1202  from start to end of a particular time interval inclusive;   calculating positive onset slope and following negative slope;   calculating time to stand from start to end of stand movement (i.e., from sitting to full upright standing position);   summing number of samples from slope increase to returning to below baseline;   calculating peak amplitude;   calculating number of events;   storing date/time of each stand up with the standing up characteristic;   calculating daily min, max, median values of any collected parameter;   calculating estimated 5× Sit-To-Stand Test (5×SST) score=[Daily median of time to stand+constant sit time (e.g. 1 sec)]×5; in some instances, assume time to sit is fairly constant due the work of gravity; other examples are possible where the score may be calculated at any particular time or interval over any particular number of sit-to-stand movements       

     Additionally, or alternatively, the example algorithm may leverage the following features:
         Symmetry: in order to stand the chest will lean forward then return to upright orientation; the symmetry between leaning forward versus return to upright might correlate with health based on a determined waveform shape;   First integral of the sagittal axis signal: integration of acceleration is the velocity (v) therefore this feature tells the (instantaneous or average) velocity when a patient sits or stands;   Second integral of the sagittal axis signal: integration of velocity is the distance, therefore this feature tells how far a patient leaned forward in order to stand up or sit, revealing at least balance capability;   Kinetic energy: assuming or measuring upper body mass (m), kinetic energy follows as mv 2 /2, which quantifies or estimates how much energy is expended during sit or stand movements;   Potential energy: this feature may require an acceleration sensor in feet to head direction which would enable us to calculate the potential energy=mgh, g is gravity. Alternatively, height, h, may be calculated from reference=0 (e.g., sitting position) to height=h as derived from a vertical axis signal (see below discussion) may be used. The energy transition between kinetic energy and potential energy may reveals health. For example, in a movement, a patient may spend 200 joules of kinetic energy to stand up and acquired 150 joules of potential energy: The ratio of 150/200 would be lowered when sick;   First derivative; derivative of acceleration is known as the jerk, which reveals sudden movement, such as sudden drops that occur in heart failure patients during seating down;   Max, min, difference, time evolution and other weighted functional combination of features above.       

     While the algorithm of  FIG. 16  is directed to an analysis of the sagittal axis signal  1202  only to determine a Sit-To-Stand score, it is contemplated that one or both of the vertical axis signal  1204  and the transverse axis signal  1206  of  FIG. 12  may be leveraged as well. For example,  FIG. 17  is a plot  1700  illustrating first characteristics of the vertical axis signal  1204  of  FIG. 12 .  FIG. 18  is a plot  1800  illustrating second characteristics of the vertical axis signal  1204  of  FIG. 17 . In practice, peak-valley information (equivalently, peak-peak information) and temporal interval information or determining patient functional status based on accelerometer-generated data in accordance with the disclosure. With reference to  FIGS. 17-18 , an example algorithm may include: 
     Identify valley (negative deflection) from baseline by:
         determining flat baseline (e.g., −1 g) for at least a certain number of second (e.g. 15 s) with a decrease of at least certain number of units (e.g. 0.2 g);       

     Identify peak (positive deflection) following valley by:
         (if valley detected), checking for peak of similar amplitude from baseline within a certain time period (e.g. within 0.5 s-5 s);       

     Determine standing up characteristics by:
         analyzing the vertical axis signal  1204  from start to end inclusive;   determining vertical negative and positive deflections for boundary/range for confirming presence of a sit-to-stand segment from the sagittal axis signal; this may be beneficial since the stand-to-sit morphology can look similar; after the vertical axis signal confirms the sit-to-stand on the sagittal axis signal, then all the sagittal measurements can be made;   calculating maximum absolute amplitude between the peak and valley;   calculating time to stand from start to stop;   calculating number of events;   storing date/time of each stand up with the standing up characteristic;   calculating daily min, max, median;   calculating estimated 5× Sit to Stand Test (5×SST) score=[Daily median of time to stand+constant sit time (e.g. 1 sec)]×5; in some instance, assume time to sit is fairly constant due the work of gravity.       

     Referring now to  FIGS. 19-28 , each one is a plot of a series of plots  1900 - 2800  illustrating sagittal, vertical and transverse axis signals produced by an accelerometer during at least one sit-stand movement. The form or shape of the respective signals in each plot of a series of plots  1900 - 2800  are consistent with sagittal axis signal  1202 , a vertical axis signal  1204  and transverse axis signal  1206  as shown in  FIG. 12 , and further demonstrate that the respective signals can be leveraged for measuring or determining patient functional status, as part of a SST performance test for example. 
       FIG. 29  is a flowchart illustrating a third example method  2900  for determining patient functional status based on accelerometer-generated data in accordance with the disclosure. Similar to method  1000 , may be implemented by any one of the implantable medical devices discussed above in connection with  FIGS. 1-9 , because each one of the same is configured to include at least one accelerometer (i.e., accelerometer circuity), as well as communication and processing circuitry (see  FIG. 7  and corresponding description) to facilitate determining patient functional status based on accelerometer-generated data. Further, method  2900  may be implemented at least in part by any one of the external devices discussed above in connection with  FIGS. 1-9 , because each one of the same is configured to include a human-machine interface (e.g., touchscreen), as well as communication and processing circuitry (see  FIG. 8  and corresponding description) to facilitate determining patient functional status based on accelerometer-generated data. 
     For example, and with reference to  FIGS. 30-33  which illustrate a sequence of graphical user interfaces for acquiring timestamps to mark a window for determining patient functional status based on accelerometer-generated data in accordance with the disclosure, a smartphone  30  ( FIG. 30 ) that comprises a pushbutton input  302 , an audio output  304  and a touchscreen  306  may prompt ( 2902 ) an end-user (i.e., patient) to engage in a “Sit-To-Stand” test. In some examples, the prompt may include one or more of a visual, an audio and a tactile output or cue. For example, together with at least one of a “beep” and a preferentially-timed “vibrate” output by the smartphone  30 , a graphic “Action: Sit-To-Stand” test in a first user interface ( FIG. 30 ) may be output for display on the touchscreen  306 . In this example, a control  310  and a control  312  may also be output for display, so that the end-user may “tap” to participate in (or not) the Sit-To-Stand test. 
     In response to a “tap” of the control  310 , an animation  316  in a second user interface ( FIGS. 31-32 ) may be output for display on the touchscreen  306 , to guide the patient to perform an appropriate movement for the Sit-To-Stand test. In this example, a control  318  and a control  320  may also be output for display, so that the end-user may “tap” to start (or not) the Sit-To-Stand test. In response to a “tap” of the control  318 , a first timestamp ( FIG. 31 ) may be generated ( 2904 ) by the smartphone  300  to mark the beginning of the Sit-To-Stand test, and presumably the beginning of a “stand” movement as per the animation  316 . Next, in response to a “tap” of the control  320 , a second timestamp ( FIG. 32 ) may be generated ( 2906 ) by the smartphone  300  to mark the end of the Sit-To-Stand test, and presumably the end of a “sit” movement as per the animation  316 . In some examples, only a single control may be presented in the second user interface so that a patient could keep a finger above a same area of the touchscreen  306 , and then perform a “tap” then “tap” action on the same area of the touchscreen  306 , to simplify the process and provoke a more accurate timestamp generation procedure. In this example, the text “Start” on the control  318  may change to the text “Stop” following a first “tap”. Other examples are possible. 
     Following acquisition of the first and second timestamp as described, corresponding data may be transmitted ( 2908 ) to the IMB  10  such that the IMD  10  may calculate ( 2910 ) a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of a sagittal axis signal, a vertical axis signal and a transverse axis signal over a time segment inclusively bounded by a first time defined by the first timestamp data and a second time defined by the second timestamp data, in a manner as discussed above. In some examples, the accelerometer(s)  166  is assumed to be powered-on and storing data associated with at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal throughout time, such as in a buffer memory of predetermined capacity (e.g., 10 minute rolling window). Accordingly, upon receipt of the first and second timestamp, the IMD  10  may access the buffer memory to access the proper time segment data to be used to calculate the patient-specific functional status parameter. In other examples, the smartphone  302  and the IMB  10  may transparently negotiate prior to the prompt ( 2902 ) mentioned above, and then the IMD  10  may power-on the accelerometer(s)  166  and/or or increase resolution of data acquired by the accelerometer(s)  166  over a forthcoming Sit-To-Stand test. Other examples are possible. 
     In some examples, data corresponding to the patient-specific functional status parameter may then be transmitted ( 2908 ) to the smartphone  302  such that a graphic “Your current score is 7 on a scale of 10” in a third user interface ( FIG. 33 ) may be output for display on the touchscreen  306 . Such an implementation may be beneficial and/or advantageous in many respects. For example, such an implementation may be used to identify changes in behavior and health, to get medical attention sooner rather than later and improving the chances of earlier recovery. Prompting an individual user will provide greater confidence that the user is performing the activity (with their unique sensor signal morphology) than a “one-size-fits-all” solution. It is contemplated that patient-specific morphology may also be used as a template to identify similar morphologies while continuously monitoring the accelerometer signal, to identify similar occurrences of sit-to-stand movements to then be analyzed with any or all of the disclosed metrics. 
       FIG. 34  is a functional block diagram  3400  illustrating an example communication sequence between the smartphone  30  (equivalently, “user equipment”) and the IMB  10  of  FIG. 29 . Although not so limited, timestamp data  3401  may be transmitted from the smartphone  30  to the IMD  10 , and at a subsequent time metric data  3403  may be transmitted to from the IMB  10  to the smartphone  30  in a manner as discussed above in connection with  FIG. 29 . As would be understood by one of skill in the art, the smartphone  30  may include communication circuitry  3402 , processing circuitry  3404 , and a touchscreen  3406 . An example of such a smartphone includes as the iPhone® by Apple Inc. of Cupertino, Calif. Regarding the smartphone  30 , additional details may be found in above discussion in connection with at least  FIG. 8 . The IMB  10  may include communication circuitry  3408 , processing circuitry  3410 , and at least one accelerometer  3412 . Regarding the IMD  10 , additional details may be found in above discussion in connection with at least  FIG. 7 , including details to the sensing circuitry  162 , processing circuitry  160 , accelerometers  167 , etc. 
     A medical device or system, method, and non-transitory computer-readable storage medium comprising executable instructions, for determining patient-specific functional status from accelerometer data is contemplated throughout. 
     For example, an implantable medical device (IMD) for determining patient-specific functional status from accelerometer data may include or comprise communication circuity configured to establish a communication link and transfer data between the IMB intra-corpus and a computing device extra-corpus. An example of such an implementation is discussed above in connection with at least  FIG. 9 . The IMD may further include or comprise accelerometer circuitry configured to generate a plurality of signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal. An example of such an implementation is discussed above in connection with at least  FIG. 7 . The IMB may further include or comprise processing circuitry configured to: calculate a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and in response to a command, activate the communication circuity to transmit the patient-specific functional status parameter from the IMD to the computing device. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 16 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate rate of change of a segment of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and calculate the patient-specific functional status parameter based on the calculated rate of change. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 14 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a definite integral over a segment of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and calculate the patient-specific functional status parameter based on the calculated definite integral. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 14 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a length of time of a segment of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and calculate the patient-specific functional status parameter based on the calculated length of time. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 16 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a peak amplitude of a segment of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and calculate the patient-specific functional status parameter based on the calculated peak amplitude. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 16 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a peak-peak amplitude of a segment of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and calculate the patient-specific functional status parameter based on the calculated peak-peak amplitude. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 17 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate at least one baseline characteristic from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal, wherein the at least one baseline characteristic is associated with a movement-free sitting position posture; and calculate the patient-specific functional status parameter based on the at least one baseline characteristic. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 15 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate the patient-specific functional status parameter from at least one characteristic of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal over multiple distinct time intervals; calculate an averaged (central tendencies: mean, median, mode) patient-specific functional status parameter from each interval-specific calculated patient-specific functional status parameter; and activate the communication circuity to transmit the averaged patient-specific functional status parameter from the IMD to the computing device. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 16 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a time occurrence of at least one inflection point of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and calculate the patient-specific functional status parameter based on the calculated time occurrence. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 15  (i.e., time at which “peak” occurs). 
     Additionally, or alternatively, the processing circuitry is configured to: in response to an activation command, power-on the accelerometer circuitry to generate the plurality of signals including the sagittal axis signal, the vertical axis signal and the transverse axis signal. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 10 . 
     Additionally, or alternatively, the processing circuitry is configured to: in response to a deactivation command, power-down the accelerometer circuitry for a predetermined period of time to conserve power of the IMD. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 10 . In some examples, deactivation may occur automatically, at a predetermined time following activation (e.g., 10 minutes), and/or following detection of at least one of: a completed sit-to-stand movement; and a completed stand-to-sit movement. 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a symmetry characteristic metric from at least one segment of the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal, wherein the at least one segment is associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and calculate the patient-specific functional status parameter based on the calculated symmetry characteristic metric. An example of such an implementation is discussed above in connection with and shown in at least  FIGS. 15-16 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a velocity metric from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal to determine an average or instantaneous velocity associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and calculate the patient-specific functional status parameter based on the calculated velocity metric. An example of such an implementation is discussed above in connection with and shown in at least  FIGS. 15-16 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a distance metric from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal to determine a total or intermediate displacement associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and calculate the patient-specific functional status parameter based on the calculated distance metric. An example of such an implementation is discussed above in connection with and shown in at least  FIGS. 15-16 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a kinetic energy metric from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal to determine a total or intermediate energy expenditure associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and calculate the patient-specific functional status parameter based on the calculated kinetic energy metric. An example of such an implementation is discussed above in connection with and shown in at least  FIGS. 15-16 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a potential energy metric from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal and the distance metric to determine a total or intermediate increase or decrease in potential energy associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and calculate the patient-specific functional status parameter based on the calculated potential energy metric. An example of such an implementation is discussed above in connection with and shown in at least  FIGS. 15-16 . 
     Additionally, or alternatively, the processing circuitry is configured to: calculate a derivative metric from a segment of the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal to determine erratic movement associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and calculate the patient-specific functional status parameter based on the calculated derivative metric. An example of such an implementation is discussed above in connection with and shown in at least  FIGS. 15-16 . 
     Additionally, or alternatively, the processing circuitry is configured to: distinguish, based on the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis, a sit-to-stand movement from a stand-to-sit movement. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 13 , whereby the shape of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis, is an indication of whether a detected movement is a sit-to-stand movement from a stand-to-sit movement. 
     Additionally, or alternatively, the processing circuitry is configured to: identify a stand-to-sit morphology from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 13 , whereby the shape of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis, is an indication of whether a detected movement is a sit-to-stand movement from a stand-to-sit movement. 
     Additionally, or alternatively, the processing circuitry is configured to: identify a sit-to-stand morphology from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis. An example of such an implementation is discussed above in connection with and shown in at least  FIG. 13 , whereby the shape of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis, is an indication of whether a detected movement is a sit-to-stand movement from a stand-to-sit movement. 
     Various aspects of the techniques may be implemented within one or more processors, including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components, embodied in programmers, such as physician or patient programmers, electrical stimulators, or other devices. The term “processor” or “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. 
     In one or more examples, the functions described in this disclosure may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on, as one or more instructions or code, a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media forming a tangible, non-transitory medium. Instructions may be executed by one or more processors, such as one or more DSPs, ASICs, FPGAs, general purpose microprocessors, or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to one or more of any of the foregoing structure or any other structure suitable for implementation of the techniques described herein. 
     In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and/or software modules. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Also, the techniques could be fully implemented in one or more circuits or logic elements. The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including an IMD, an external programmer, a combination of an IMD and external programmer, an integrated circuit (IC) or a set of ICs, and/or discrete electrical circuitry, residing in an IMD and/or external programmer. 
     Exemplary Embodiments 
     Embodiment 1 is a medical device system comprising:
         accelerometer circuitry configured to generate a plurality of signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal; and   processing circuitry configured to:
           calculate a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal.   
               

     Embodiment 2 is a method comprising:
         generating, by a medical device system, a plurality of signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal; and   calculating, by the medical device system, a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal.       

     Embodiment 3 is a medical device system comprising means for performing the method of embodiment 2. 
     Embodiment 4 is a non-transitory computer-readable storage medium comprising instructions, that when executed by processing circuitry of a medical device system, cause the medical device system to perform the method of embodiment 2. 
     Embodiment 5 is a medical device system, method, and non-transitory computer-readable storage medium comprising executable instructions, for determining patient-specific functional status from accelerometer data as described in the specification and/or shown in any of the drawings. 
     Embodiment 6 is a method comprising:
         by an implantable medical device, intra-corpus,   generating a plurality of signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal;   calculating a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and   transmitting the patient-specific functional status parameter to a computing device extra-corpus.       

     Embodiment 7 is the method of embodiment 6, further comprising:
         calculating rate of change of a segment of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and   calculating the patient-specific functional status parameter based on the calculated rate of change.       

     Embodiment 8 is the method of embodiment 6 or 7, further comprising:
         calculating a definite integral over a segment of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and   calculating the patient-specific functional status parameter based on the calculated definite integral.       

     Embodiment 9 is the method of any of embodiments 6 to 8, further comprising:
         calculating a length of time of a segment of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and   calculating the patient-specific functional status parameter based on the calculated length of time.       

     Embodiment 10 is the method of any of embodiments 6 to 9, further comprising:
         calculating a peak amplitude of a segment of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and   calculating the patient-specific functional status parameter based on the calculated peak amplitude.       

     Embodiment 11 is the method of any of embodiments 6 to 10, further comprising:
         calculating a peak-peak amplitude of a segment of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and   calculating the patient-specific functional status parameter based on the calculated peak-peak amplitude.       

     Embodiment 12 is the method of any of embodiments 6 toll, further comprising:
         calculating at least one baseline characteristic from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal, wherein the at least one baseline characteristic is associated with a movement-free sitting position posture; and   calculating the patient-specific functional status parameter based on the at least one baseline characteristic.       

     Embodiment 13 is the method of any of embodiments 6 to 12, further comprising:
         calculating the patient-specific functional status parameter from at least one characteristic of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal over multiple distinct time intervals;   calculating an averaged patient-specific functional status parameter from each interval-specific calculated patient-specific functional status parameter; and transmitting the averaged patient-specific functional status parameter to the computing device.       

     Embodiment 14 is the method of any of embodiments 6 to 13, further comprising:
         calculating a time occurrence of at least one inflection point of at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and   calculating the patient-specific functional status parameter based on the calculated time occurrence.       

     Embodiment 15 is the method of any of embodiments 6 to 14, further
         comprising:   in response to an activation command, powering-on accelerometer circuitry of the implantable medical device to generate the plurality of signals including the sagittal axis signal, the vertical axis signal and the transverse axis signal.       

     Embodiment 16 is the method of any of embodiments 6 to 15, further
         comprising:   in response to a deactivation command, powering-down accelerometer circuitry of the implantable medical device for a predetermined period of time to conserve power of the implantable medical device.       

     Embodiment 17 is the method of embodiment 6, further comprising:
         calculating a symmetry characteristic of at least one segment from the at least one of the sagittal calculate a symmetry characteristic metric from at least one segment of the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal, wherein the at least one segment is associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and   calculating the patient-specific functional status parameter based on the calculated symmetry characteristic metric.       

     Embodiment 18 is the method of embodiment 17, further comprising:
         calculating a velocity metric from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal to determine an average or instantaneous velocity associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and   calculating the patient-specific functional status parameter based on the calculated velocity metric.       

     Embodiment 19 is the method of any of embodiments 17 to 18, further
         comprising:   calculating a distance metric from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and   calculating the patient-specific functional status parameter based on the calculated distance metric.       

     Embodiment 20 is The method of any of embodiments 18 to 20, further
         comprising:   calculating a kinetic energy metric from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and   calculating the patient-specific functional status parameter based on the calculated kinetic energy metric.       

     Embodiment 21 is the method of any of embodiments 18 to 20, further
         comprising:   calculating a potential energy metric from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal and the distance metric to determine a total or intermediate increase or decrease in potential energy associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and   calculating the patient-specific functional status parameter based on the calculated potential energy metric.       

     Embodiment 22 is the method of any of claims  18  to  21 , further comprising:
         calculating a derivative metric from a segment of the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal and the distance metric to determine erratic movement associated with a Sit-To-Stand or stand-to-sit movement of the Sit-To-Stand test; and   calculating the patient-specific functional status parameter based on the calculated derivative metric.       

     Embodiment 23 is an implantable medical device comprising means for performing any of the methods of claims  6  to  22 . 
     Embodiment 24 is a non-transitory computer-readable storage medium comprising instructions, that when executed by processing circuitry of an implantable medical device, cause the implantable medical device to perform any of the methods of claims  6  to  22 . 
     Embodiment 25 is a method comprising:
         by an implantable medical device, intra-corpus,   generating a plurality of signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal;   calculating a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and   transmitting the patient-specific functional status parameter to a computing device extra-corpus.       

     Embodiment 26 is the method of embodiment 25, further comprising:
         distinguishing, based on the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis, a sit-to-stand movement from a stand-to-sit movement.       

     Embodiment 27 is the method of any of embodiments 25 to 26, further
         comprising:   identifying a stand-to-sit morphology from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis.       

     Embodiment 28 is the method of any of claims  25  to  27 , further comprising:
         identifying a sit-to-stand morphology from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis.       

     Embodiment 29 is an implantable medical device comprising:
         communication circuity configured to establish a communication link and transfer data between the IMD intra-corpus and a computing device extra-corpus;   accelerometer circuitry configured to generate a plurality of signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal; and   processing circuitry configured to:   calculate a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal; and   activate the communication circuity to transmit the patient-specific functional status parameter from the IMD to the computing device.       

     Embodiment 30 is the device of embodiment 29, wherein the processing
         circuitry is configured to:   distinguish, based on the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis, a sit-to-stand movement from a stand-to-sit movement.       

     Embodiment 31 is the device of any of embodiments 29 to 30, wherein the
         processing circuitry is configured to:   identify a stand-to-sit morphology from the at least one of the sagittal axis signal, the vertical axis signal and the transverse axis.       

     Various aspects of the disclosure have been described. These and other aspects are within the scope of the following claims.