Abstract:
A method and system for activity monitoring of a user are disclosed. In a first aspect, the method comprises calibrating posture by the user to determine a calibration vector. The method includes validating the calibration vector by comparing an anteroposterior axis to a threshold, wherein activity of the user is monitored using the validated calibration vector. In a second aspect, a wireless sensor device comprises a processor and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to receive a posture calibration request from the user and to determine a calibration vector based on the received request. The application, when executed by the processor, further causes the processor to validate the calibration vector by comparing an anteroposterior axis to a threshold, wherein activity of the user is monitored using the validated calibration vector.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates to wireless sensor devices, and more particularly, to using a wireless sensor device to calibrate posture for activity monitoring. 
       BACKGROUND 
       [0002]    Wireless sensor devices are used in a variety of applications including the activity monitoring of users. In many of these applications, a wireless sensor device is attached directly to the user&#39;s skin to measure certain data. This measured data is then utilized for the activity monitoring of the users. Therefore, there is a strong need for a cost-effective solution that more accurately calibrates posture for the activity monitoring of a user. The present invention addresses such a need. 
       SUMMARY OF THE INVENTION 
       [0003]    A method and system for activity monitoring of a user are disclosed. In a first aspect, the method comprises calibrating posture by the user to determine a calibration vector. The method includes validating the calibration vector by comparing an anteroposterior axis to a threshold, wherein activity of the user is monitored using the validated calibration vector. 
         [0004]    In a second aspect, a wireless sensor device comprises a processor and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to receive a posture calibration request from the user and to determine a calibration vector based on the received request. The application, when executed by the processor, further causes the processor to validate the calibration vector by comparing an anteroposterior axis to a threshold, wherein activity of the user is monitored using the validated calibration vector. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0005]    The accompanying figures illustrate several embodiments of the invention and, together with the description, serve to explain the principles of the invention. One of ordinary skill in the art readily recognizes that the particular embodiments illustrated in the figures are merely exemplary, and are not intended to limit the scope of the present invention. 
           [0006]      FIG. 1  illustrates a wireless sensor device in accordance with an embodiment. 
           [0007]      FIG. 2  illustrates a flow chart of a method in accordance with an embodiment. 
           [0008]      FIG. 3  illustrates a more detailed flow chart of a method in accordance with an embodiment. 
           [0009]      FIG. 4  illustrates a diagram of calibration vector checking in accordance with an embodiment. 
           [0010]      FIG. 5  illustrates a diagram of an example of explicit calibration in accordance with an embodiment. 
           [0011]      FIG. 6  illustrates a diagram of an example of implicit calibration in accordance with an embodiment. 
       
    
    
     DETAILED DESCRIPTION 
       [0012]    The present invention relates to wireless sensor devices, and more particularly, to using a wireless sensor device to calibrate posture for activity monitoring. The following description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a patent application and its requirements. Various modifications to the preferred embodiment and the generic principles and features described herein will be readily apparent to those skilled in the art. Thus, the present invention is not intended to be limited to the embodiments shown but is to be accorded the widest scope consistent with the principles and features described herein. 
         [0013]    A method and system in accordance with the present invention automatically calibrates a user&#39;s posture for activity monitoring. By attaching a wireless sensor device in any orientation and in any bodily location to the user and comparing a calibration vector to a vertical posture, a user&#39;s posture is automatically calibrated and used in algorithms that measure activity level including but not limited to pedometer activity, fall detection, and posture detection. 
         [0014]    One of ordinary skill in the art readily recognizes that a variety of wireless sensor devices can be utilized including but not limited to tri-axial accelerometers, uni-axial accelerometers, bi-axial accelerometers, gyroscopes, and pressure sensors and that would be within the spirit and scope of the present invention. 
         [0015]    To describe the features of the present invention in more detail, refer now to the following description in conjunction with the accompanying Figures. 
         [0016]    In one embodiment, a wireless sensor device is attached to a user and continuously and automatically obtains varying types of data including but not limited to acceleration samples along an anteroposterior axis of the user. An application embedded within a processor of the wireless sensor device compares the acceleration samples to a threshold to monitor the user&#39;s activity including but not limited to pedometer activity, fall detection, and posture detection. 
         [0017]      FIG. 1  illustrates a wireless sensor device  100  in accordance with an embodiment. The wireless sensor device  100  includes a sensor  102 , a processor  104  coupled to the sensor  102 , a memory  106  coupled to the processor  104 , an application  108  coupled to the memory  106 , and a transmitter  110  coupled to the application  108 . In one embodiment, the wireless sensor device  100  is attached, in any orientation to a user and on any location of the user. The sensor  102  obtains data from the user and transmits the data to the memory  106  and in turn to the application  108 . The processor  104  executes the application  108  to monitor information regarding the user&#39;s activity. The information is transmitted to the transmitter  110  and in turn relayed to another user or device. 
         [0018]    In one embodiment, the sensor  102  is a microelectromechanical system (MEMS) tri-axial accelerometer and the processor  104  is a microprocessor. One of ordinary skill in the art readily recognizes that a variety of devices can be utilized for the processor  104 , the memory  106 , the application  108 , and the transmitter  110  and that would be within the spirit and scope of the present invention. 
         [0019]      FIG. 2  illustrates a flow chart of a method  200  in accordance with an embodiment. Referring to  FIGS. 1 and 2  together, the method  200  comprises calibrating posture by the user to determine a calibration vector, via step  202 . The wireless sensor device  100  validates the calibration vector by comparing an anteroposterior axis to a threshold and monitors the activity of the user using the validated calibration vector, via step  204 . In one embodiment, the sensor  102  that is housed within the wireless sensor device  100  measures the anteroposterior axis of the user. In another embodiment, notification information of the activity monitoring of the user is relayed by the wireless sensor device  100  to another user or device. 
         [0020]      FIG. 3  illustrates a more detailed flow chart of a method  300  in accordance with an embodiment. A wireless sensor device is attached to a user in any orientation and on any bodily location of the user. Referring to  FIGS. 1 and 3  together, the method  300  comprises an explicit calibration of the user&#39;s posture to determine a calibration vector, via step  302 . In one embodiment, calibrating the user&#39;s posture explicitly includes but is not limited to the user notifying the wireless sensor device  100  when the user is in a vertical position and the wireless sensor device  100  being attached to the user&#39;s chest when the user is in a vertical position. 
         [0021]    In this embodiment, the user notifies the wireless sensor device  100  in a variety of ways including but not limited to tapping the wireless sensor device  100 , selecting a button of the wireless sensor device  100 , and interacting with a mobile application interface of the wireless sensor device  100 . Furthermore, in this embodiment, when the wireless sensor device  100  is attached to the user&#39;s chest while the user is in a vertical position, the wireless sensor device  100  recognizes contact impedance to confirm attachment between the user and the wireless sensor device  100 . 
         [0022]    The wireless sensor device  100  checks to see whether the explicitly determined calibration vector is valid, via step  304 . If the determined calibration vector is valid, the wireless sensor device  100  sets cal_wrong_flag to zero (0), uses a vertical acceleration based on the validated calibration vector in both pedometer activity and fall detection algorithms, and confirms posture detection is valid, via step  306 . 
         [0023]    In  FIG. 3 , if the explicitly determined calibration vector is not valid, the wireless sensor device  100  displays a validation failure message to the user prompting the user to determine whether the user wants to explicitly recalibrate another calibration vector, via step  308 . If the user wants to explicitly recalibrate another calibration vector, the method  300  returns back to step  302 . If the user does not want to explicitly recalibrate another calibration vector, the method  300  sets cal_wrong_flag to one (1), uses a norm of acceleration in both pedometer activity and fall detection algorithms, and sets posture detection to unknown, via step  310 . In this embodiment, when the determined calibration vector is not valid, the wireless sensor device  100  monitors the activity of the user using a set of algorithms that are independent of the calibration vector. 
         [0024]    In one embodiment, the determined calibration vector is checked for validity by ensuring a magnitude of acceleration along an anteroposterior axis of the user is less than a predetermined threshold including but not limited to g*sin(π/6), where g is the acceleration due to gravity. In this embodiment, if the magnitude of acceleration along the anteroposterior axis of the user is less than the predetermined threshold, then the calibration vector is determined to be valid and the method  300  proceeds to step  306 . However, if the magnitude of acceleration along the anteroposterior axis of the user is greater than or equal to the predetermined threshold, then the calibration vector is determined to be invalid and the method  300  proceeds to step  308 . The anteroposterior axis of the user measures the axis from the front chest to the back of the user and is nearly perpendicular to gravity when the user is in a vertical posture. 
         [0025]      FIG. 4  illustrates a diagram  400  of calibration vector checking in accordance with an embodiment. The diagram  400  includes a first scenario  402  where the calibration check passes because an absolute value of acceleration along the anteroposterior axis of the user is less than the predetermined threshold. The diagram  400  includes a second scenario  404  where the calibration check fails because an absolute value of acceleration along the anteroposterior axis of the user is greater than or equal to the predetermined threshold. 
         [0026]    Referring back to  FIG. 3 , after both steps  310  and  306 , the user&#39;s activity including but not limited to pedometer activity, fall detection, and posture detection is monitored using various algorithms depending upon whether the calibration vector is validated or not, via step  312 . In one embodiment, monitoring the activity of the user using the validated calibration vector includes but is not limited to monitoring pedometer activity using a vertical component of an acceleration vector of the user, monitoring fall detection using p-norm of the acceleration vector to detect an impact and an angle of the acceleration vector with respect to the validated calibration vector thereby determining a horizontal position of the user after impact, and monitoring posture detection using both the acceleration vector and the validated calibration vector. 
         [0027]    In another embodiment, monitoring the activity of the user using a non-validated calibration vector due to a validation failure includes but is not limited to monitoring pedometer activity using 2-norm of an acceleration vector of the user and monitoring fall detection using p-norm of the acceleration vector to detect an impact. In this embodiment, the monitoring of fall detection does not compute an angle and the posture of the user is unknown. Accordingly, it is desirable to monitor the activity of the user using a validated calibration vector. 
         [0028]    Therefore, the activity algorithms utilized by the wireless sensor device  100  vary when using a validated calibration vector and when not using a validated calibration vector. In one embodiment, current acceleration (a) and calibration vectors (c) are utilized by the wireless sensor device  100  in the activity algorithms with a=(ax, ay, az) and c=(cx, cy, cz) when the calibration vector is validated. 
         [0029]    In this embodiment, the activity algorithms that include a validated calibration vector comprise a pedometer activity algorithm that is based on a vertical component of the acceleration vector (pedometer activity (v)=a·c=ax*cx+ay*cy+az*cz), a fall detection algorithm that is based on p-norm of a to detect an impact and angle of a with respect to c to determine a horizontal position of the user after impact (p-norm of a=(|ax|̂p+|ay|̂p+|az|̂p)̂(1/p), for p&gt;=1; angle of a computed using a·c and 2-norms of a and c), and a posture detection algorithm that is based on a·c, cz and az. 
         [0030]    Furthermore, in another embodiment, the activity algorithms that are utilized by the wireless sensor device  100  when not using a validated calibration vector include but are not limited to a pedometer activity algorithm that is based on 2-norms of a, a fall detection algorithm that is based on p-norm of a to detect an impact where no angle of a is computed, and no posture detection algorithm because the posture of the user is unknown. 
         [0031]    In the method  300 , once footsteps of the user are detected by a pedometer type device that has been integrated into the wireless sensor device  100 , via step  314 , the wireless sensor device  100  utilizes implicit calibration to determine a new calibration vector. In one embodiment, the implicit calibration includes but is not limited to the wireless sensor device  100  deriving a vertical position based on an acceleration vector corresponding to footsteps when the user is walking. After the implicit calibration, the method  300  checks to see whether cal_wrong_flag is equal to one (1), via step  316 . 
         [0032]    If cal_wrong_flag is equal to one (1) indicating that the wireless sensor device  100  has been monitoring the activity of the user using a non-validated calibration vector, the method  300  returns back to step  302  to validate the new calibration vector. If cal_wrong_flag is not equal to one (1), indicating that the wireless sensor device  100  has been monitoring the activity of the user using a validated calibration vector, the method  300  returns back to step  312  and the wireless sensor device  100  continues the activity monitoring of the user. 
         [0033]      FIG. 5  illustrates a diagram  500  of an example of explicit calibration in accordance with an embodiment. The diagram  500  plots anteroposterior acceleration, step count, and posture of the user over a predetermined time period. The diagram  500  starts with a valid explicit calibration  502  corresponding to a known standing posture  504  of the user. The explicit calibration is valid because the user is in a standing posture when the user has notified the wireless sensor device to explicitly calibrate or the wireless sensor device has been attached to the user while in a standing posture. 
         [0034]    As the step count of the user increases, the anteroposterior acceleration fluctuates and the posture of the user is identified to be in a walking posture. At approximately sixty (60) seconds, the step count of the user doesn&#39;t increase anymore thereby illustrating another change in the user&#39;s posture. 
         [0035]      FIG. 6  illustrates a diagram  600  of an example of implicit calibration in accordance with an embodiment. The diagram  600  plots anteroposterior acceleration, step count, and posture of the user over a predetermined time period. The diagram  600  starts with an invalid explicit calibration  602  corresponding to an unknown posture  604  of the user. The explicit calibration is invalid because the user is in an unknown posture when the user has notified the wireless sensor device to explicitly calibrate or the wireless sensor device has not been attached to the user while in a standing posture. 
         [0036]    As aforementioned, due to this invalid explicit calibration, the wireless sensor device attached to the user will monitor the user&#39;s activity by utilizing activity algorithms that do not incorporate a calibration vector. As the step count of the user increases, implicit calibration while walking  606  occurs to incorporate a newly determined calibration vector into the activity algorithms utilized by the wireless sensor device. At this time while the user is walking, which is at approximately seventy (70) seconds, the wireless sensor device computes a known posture of the user  608 . 
         [0037]    As above described, the method and system allow for activity monitoring based upon automatic posture calibration. By implementing a tri-axial accelerometer within a wireless sensor device to detect acceleration and posture samples and an application located on the wireless sensor device to process the detected acceleration and posture samples, an efficient and cost-effective activity monitoring system is achieved that can support various types of activities and can confirm changes in a user&#39;s posture. 
         [0038]    A method and system for activity monitoring of a user have been disclosed. Embodiments described herein can take the form of an entirely hardware implementation, an entirely software implementation, or an implementation containing both hardware and software elements. Embodiments may be implemented in software, which includes, but is not limited to, application software, firmware, resident software, microcode, etc. 
         [0039]    The steps described herein may be implemented using any suitable controller or processor, and software application, which may be stored on any suitable storage location or computer-readable medium. The software application provides instructions that enable the processor to perform the functions described herein. 
         [0040]    Furthermore, embodiments may take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer-readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 
         [0041]    The medium may be an electronic, magnetic, optical, electromagnetic, infrared, semiconductor system (or apparatus or device), or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk, and an optical disk. Current examples of optical disks include DVD, compact disk-read-only memory (CD-ROM), and compact disk-read/write (CD-R/W). 
         [0042]    Although the present invention has been described in accordance with the embodiments shown, one of ordinary skill in the art will readily recognize that there could be variations to the embodiments and those variations would be within the spirit and scope of the present invention. Accordingly, many modifications may be made by one of ordinary skill in the art without departing from the spirit and scope of the appended claims.