Patent Description:
Humans exhibit four major vital signs that are used to provide a comprehensive picture of an individual's bodily vital functions. These four vital signs (body temperature, heart rate, breath rate, and blood pressure) of an individual allow caregivers to assess whether further measures are to be taken with regards to an individual's overall health. Being able to obtain any of the four vital signs without being tethered to monitoring equipment invites novel and useful ways for humans to keep track of their health. However, monitoring vitals without having the subject in physical contact with monitoring equipment may be difficult, costly, or may produce unreliable data.

<CIT> describes methods and systems for monitoring vital signs for the prediction and treatment of physiological ailments.

<CIT> describes a method and system for detecting sleep phenomena including sleep cycles and sleep disorders such as sleep apnea and hypopnea.

A novel approach to heart and respiratory rate monitoring is achieved by utilizing ballistocardiographs. Ballistocardiographs are graphical representations of the movement of a person's body based on an upward recoil caused by the movement of blood through the heart. This low frequency vital sign can be measured using noninvasive methods.

However, measuring and recording this vital sign (e.g., heart rate) of a person is difficult using noninvasive methods as the signal is easily overcome by noise and external vibration. To maintain signal integrity, typically, a user or a person whose vital signals are being monitored is in direct contact with measurement equipment and for the user to be as still as possible. The technical problem of obtaining a user's vital sign without requiring the user to remain still and be in direct contact with equipment begs to be solved.

Accordingly, the inventors have recognized, among other things, a need for a contact-free, noninvasive technique for monitoring vital signs of a user and have discovered a technical solution to this technical problem. Non-contact vital signal monitoring using microelectromechanical (MEMS) accelerometers allows measurements of heart and respiratory rates to be made without requiring direct contact with the user or person being monitored. The signals measured by one or more low noise, MEMS accelerometers are filtered to detect and monitor the heart and or respiratory signal of the user while the person being monitored is sitting or even lying down in a chair or in a bed. In addition to obtaining measurements of heart and respiratory rates, the embodiments of the disclosure herein contemplate capturing additional heart and lung related metrics including, but not limited to snoring and sleep apnea events. The invention provides a method according to claim <NUM>, a signal acquisition system according to claim <NUM> and a non-transitory computer readable storage medium according to claim <NUM>. The scope of the invention is defined by the appended claims For better understanding of the invention the present disclose provides also further examples, which do not fall under the scope of the claims.

In some embodiments, a method includes receiving a first signal from a first accelerometer channel, the first signal including information indicative of a physiologic signal obtained extra-corporeally. The method further includes receiving a second signal from a second accelerometer channel. The second signal includes information indicative of the physiologic signal obtained extra-corporeally. The method further includes identifying a base frequency of the physiologic signal using at least one harmonic template from a plurality of harmonic templates from at least one of the first and second signals. For at least one of the first and second signals or an aggregated representation thereof, apply a filter to establish a tracking signal. the tracking signal is initialized with the identified base frequency. The method further includes storing and/or transmitting the tracking signal as a representation of the physiologic signal.

In some embodiments, a signal acquisition system configured to track a physiologic signal includes a first accelerometer having a first accelerometer channel configured to obtain information of a physiologic signal obtained extra-corporeally and a second accelerometer having a second accelerometer channel distinct from the first accelerometer channel configured to obtain information of the physiologic signal obtained extra-corporeally. The signal acquisition system also includes a control circuit configured to receive a first data signal from the first accelerometer channel, receive a second data signal from the second accelerometer channel, and identify a base frequency of the physiologic signal from at least one of the first and second signals. The harmonic template is one of a plurality of harmonic templates. The control circuit of the signal acquisition system also applies a filter to at least one of the first and second signals or an aggregated representation thereof, to establish a tracking signal. The tracking signal is initialized with the identified base frequency. The control circuit of the signal acquisition system is further configured to store or transmit the tracking signal as a representation of the physiologic signal.

Method examples described herein can be machine or computerimplemented at least in part.

To easily identify the discussion of any element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

The present inventors have recognized, among other things, that monitoring of physiologic signals can present various challenges. For example, in one approach, such monitoring can require a subject to be physically tethered (via cables or body-worn sensors) to a machine. The user may also have to remain relatively still, or the acquisition of such signals may be corrupted.

The present subject matter concerns, among other things, apparatus, and techniques for monitoring physiologic signals such as heart rate or respiratory rates. Such monitoring is performed without requiring a subject being monitored to be physically tethered to a machine.

Accordingly, the present disclosure describes a method and systems for non-contact vital sign monitoring that allows detection, recordation, and/or transmission of human vital signs without necessitating physical contact with measurement equipment.

Reference will now be made to embodiments, examples of which are illustrated in the accompanying drawings. In the following description, numerous specific details are set forth to provide an understanding of the various described embodiments.

It will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first accelerometer could be termed a second accelerometer, and, similarly, a second accelerometer could be termed a first accelerometer, without departing from the scope of the various described embodiments. The first accelerometer and the second accelerometer are both accelerometers, but they are not necessarily the same accelerometer.

The terminology used in the description of the various described embodiments herein is for the purpose of describing embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended aspects, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and/or" as used herein refers to and encompasses all possible combinations of one or more of the associated listed items. It will be further understood that the terms "includes," "including," comprises," and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The term "exemplary" is used herein in the sense of "serving as an example, instance, or illustration" and not in the sense of "representing the best of its kind.

<FIG> illustrates a diagram of an example environment including a vital signal monitoring system in accordance with some embodiments.

System <NUM> includes a bed frame <NUM>, a mattress <NUM>, and a non-contact vital signal monitoring system <NUM>. As described in more detail herein, the system <NUM> includes a setup for a patient (e.g., user) to recline or rest in a supine, prone, right/left lateral recumbent, seated, or other various positions on the mattress <NUM>. In some embodiments, the non-contact vital signal monitoring system <NUM> is embedded within the mattress <NUM>. In some other embodiments, the non-contact vital signal monitoring system <NUM> is positioned proximate the mattress (e.g., on or within the bed frame <NUM>, not shown).

In some embodiments, one or more accelerometers are configured at a specified location on a bed. The observed configuration is for two accelerometers on top of the mattress, close to a location where a user would lie and another accelerometer on the ground. The accelerometer on the ground is still within a reasonable range to detect vital signs from the person on the bed. In some embodiments, additional sensors and accelerometers may be placed proximate the mattress in various configurations. For example, an additional accelerometer may be placed beside the patient within a first range (e.g., <NUM> inches away from the user) primarily for noise filtration. The accelerometer data from the one or more accelerometers can be used to filter out the outside vibrations (e.g., noise) by aggregating the data sets from the one or more accelerometers. After the data signals are process through one or more filters, the physiologic signal can be recovered by using known harmonic templates of the physiologic signal. For example, a known heart rate for a user/patient is within a range of <NUM> to <NUM> and has a specific wave form. This known heart rate is used as a harmonic template of the tracked physiologic signal to differentiate the tracked (e.g., physiologic) signal from the received signal.

In some embodiments, the configuration of the one or more accelerometers includes mounting the one or more accelerometers to a rigid plate to reduce noise and vibrations. One or more additional accelerometers are used to filter out additional external noise (e.g., walking, clapping, shouting sounds). The one or more additional accelerometers are useful to enhance the signal quality and to improve fidelity of the tracked signal.

The system may include one or more low noise accelerometers (e.g., ADXL355 available from Analog Devices, Inc. , Wilmington, MA, USA, or another accelerometer). The system may also include one or more electronic devices having a processor configured to receive, process, transmit, and/or analyze data from the one or more low noise accelerometers. The one or more accelerometers may be configured on a rigid structure and placed in distinct locations with respect to the other one or more accelerometers. The mechanical movements of the heart can be detected by one or more accelerometers placed within a reasonable distance of a patient or user of the device without being in direct contact with the device. Each of the one or more accelerometers are coupled to a processor to ensure the raw signals are timestamped and recorded. In this manner, the accelerometer can operate as a ballistocardiograph to measure impact and the acceleration, such as caused by the mechanical movements of the heart.

The non-contact vital signal monitoring system may include one or more accelerometers (e.g., <NUM>st, <NUM>nd, <NUM>rd eval boards) in communication with and/or coupled to a microcontroller (e.g., STM32 Nucleo-<NUM>) via insulation-displacement contact (IDC) connectors.

In some embodiments, the vital signal monitoring system may be coupled to various external devices for further analysis and/or distribution. In such embodiments, the non-contact vital signal monitoring system may include one or more cables coupling the monitoring system to the various external devices.

Additional considerations include introducing a rigid plastic plate beneath one or more of the accelerometers to house the accelerometer, signal processing and data transmission device to increase the sensitivity. This can be achieved by physically affixing the housing to the frame of a chair and/or a bed.

Additionally, or alternatively, one or more additional accelerometers may be added to the system or used in tandem with the current system to provide further noise filtration.

Additionally, or alternatively, various other vital signals can be obtained through this method and system including blood pressure, body temperature, and others.

Additionally, or alternatively, the system can be further configured to provide early warning scores and sleep quality indicators based on the tracked heart rate and/or respiration rate.

<FIG> illustrates a block diagram of the system including heart rate and heart rate variability pipeline <NUM>, and respiration rate pipeline <NUM> in accordance with some embodiments.

Block diagram <NUM> shows an overview of the system for implementing non-contact vital signal monitoring to determine a heart rate, a heart rate variability, and a respiration rate using a frequency-domain heart rate estimator. The block diagram <NUM> illustrates how a received ballistocardiogram (BCG) signal <NUM> is processed to provide a heart rate <NUM>, a heart rate variability <NUM>, and a respiration rate <NUM> in accordance with some embodiments.

For each of the heart rate <NUM>, heart rate variability <NUM>, and respiration rate <NUM> estimators, a one or more pre-processing operations <NUM> and/or <NUM> including detrending operations are performed on the raw input signal to remove the effects of gravity and slow variation from the ballistocardiogram signal <NUM>. In some embodiments, the pre-processing operations <NUM> and pre-processing modules <NUM> including detrending operations are performed independently for the heart rate and heart rate variability estimation and a separate operation is performed for the respiration rate estimation. In some embodiments, for each of the estimators, a corresponding energy detection module monitors user's status and is configured to determine if the signal becomes unusable. In the instances the signal of the estimators becomes unstable, the trackers reset, and the output of the estimators become undefined for some time while the trackers reset.

To process the received signal for each estimator, the signal is (i) filtered through a bandpass filter, (ii) processed through a heartbeat time estimator, and (iii) processed through a heart rate and/or heart rate variability calculator. First, the signal is filtered through a bandpass filter (e.g., a fourtap Butterworth bandpass filter) between a specified frequency (e.g., <NUM>-<NUM> Hertz). The bandpass filter is applied on the signal in the forwards and backwards direction. The output of the filter is a signal with a squared magnitude response also having a non-linear phase response having been zeroed out.

Second, the filtered signal is used to estimate the heartbeat times. To do so, a peak-trough pattern is identified in the time domain. In some embodiments, a signal sample is determined to be at a peak if the signal is larger or equal to its immediate neighbors. The determined peak value is validated if the peak value is large in comparison to neighboring values within at least two, time windows. Of the two-time windows, one is a smaller window and covers a duration less than an average heartbeat. The smaller window serves to identify the main peak. A second window is a larger window and covers a duration of multiple heart beats. This larger second window serves to filter out spurious peaks of signal activity.

The beat times are identified as being validated signal points with a peak-trough or trough-peak pattern occurring in quick succession (e.g., within <NUM> second). In some embodiments either of the peak-to-trough or trough-to-peak patterns are determined as being the heartbeat time. The successive difference between the heartbeat time is calculated as the beat-to-beat interval. In some embodiments, to filter out outliers, a local median of beat-to-beat intervals is computed, smoothed, and any intervals lying outside of a predetermined range (e.g., +/- <NUM>%) is rejected.

Using the calculated beat-to-beat intervals, a heart rate is estimated as the reciprocal of the median beat-to-beat time interval within a time window (e.g., the most recently recorded <NUM> seconds of the signal). The heart rate variability is estimated as being the average absolute difference from the median in a desired time window (e.g., the most recently recorded <NUM> seconds). In some embodiments, a constant is added to the heart rate variability estimate to compensate for the downward bias of using the median value. Additionally, the median value can in some embodiments be discontinuous. To assuage the discontinuity, a <NUM>-tap, exponential decay infinite impulse response (IIR) filter is used to smooth the discontinuity.

Additional operations can be performed on the signal including blind equalization. The harmonic stack of the heart signal exhibits varying resonances depending on the user's position, orientation, body type, mattress characteristics, bed frame characteristics, and others. To generalize these varying conditions, a learned finite impulse response (FIR) filter is applied to flatten out the spectral envelope of the harmonic stack. In some embodiments, a least-squares optimization of a linear phase FIR filter is applied on a segment of the signal (e.g., <NUM> seconds). This process of blind equalization sharpens the heartbeat peaks of the signal in the time-domain. Further, in some embodiments, axis combination is performed to obtain valuable data from the received signal. The X-axis of the hart signal is seen to have a peak in a positive direction when the accelerometer's x-axis is oriented toward the foot of the bed. Combining the signal of the x-axis with the signal of the y-axis and/or the z-axis, yields further information including in some embodiments, a suggestion of a lateral ballistic force being exerted by the user's heart. Additional information includes a possibility of a physical setup introducing delay into the signal that is axisdependent and is to be accounted for.

The respiration signal processing pipeline is run at <NUM> or lower. Typically, the respiration signal energy is below <NUM> and is sinusoidal. As described above with respect to detrending the signal, a lowpass filter is applied (e.g., <NUM>-tap lowpass Butterworth filter) to remove any frequencies above <NUM>. Subsequently, the peaks and troughs of the signal are identified (similar to the operations in heartbeat detection). Further, a peak-to-peak and trough-to-trough time is calculated for the respiration signal and merged together. From the time intervals of the peak-to-peak and trough-to-trough, a respiration rate can be estimated as the reciprocal of the media time interval in a ageionated time window. The median time interval can be discontinuous and, in some embodiments, smoothed using a <NUM> tap exponential decay IIR filter.

The methods and systems described herein are capable of tuning and being tuned to obtain high-quality signals and subject-to-sensor coupling. Various parameters for tuning include adjustable minimum and maximum signal power thresholds, and minimum and maximum heart rate, heart rate variability, and respiration rate.

In an example, a user (e.g., patient) is situated on a mattress with an embedded sensing mechanism (e.g., non-contact vital signal monitoring system <NUM>). There are three estimators operating within the embedded sensing mechanism while the user is situated on a mattress. A first energy detector for a heart rate pipeline (e.g., heart rate <NUM>) recursively processes the received signal, sample-by-sample. The heart rate and heart rate variability estimations are conducted at a low sampling rate (e.g., <NUM>). The heartbeat detector <NUM> may perform peak detection, peak validation, and the HR calculation <NUM> module may, using the output of the heartbeat detector <NUM>, perform identification of peak-trough pairs, beat-to-beat internal validation, and compute the heartbeat rate (e.g., in beats per minute). The heart energy detector <NUM> may in some embodiments, share similar functionalities with the heartbeat detector <NUM>. heart energy detector <NUM> may also perform functionalities including beat-to-beat validation. The output of the heart energy detector <NUM> is submitted to HRV calculation <NUM> that performs a calculation to determine the heart rate variability of the obtained signal. In some embodiments, the HRV calculation <NUM> module receives one or more signals, data samples, or other information from either or both heartbeat detector <NUM> and heart energy detector <NUM> modules.

A second energy detector for a respiration rate pipeline (e.g., respiration rate <NUM>) similarly to the heart rate pipeline, recursively processes the received signal, sample-by-sample. The respiration rate pipeline includes signal pre-process module <NUM> which may include down sampling by applying a lowpass filter and subsampling from a higher frequency to a lower frequency (e.g., from <NUM> down to <NUM>). Additional pre-process module <NUM> operations include applying filters as needed. The breath detector <NUM> module may perform operations including identifying peaks and troughs. The respiration energy detector <NUM> module may perform operations including calculating respiration signal energy and thereby determining what portions of the signal are usable for estimating respiration rate. The respiration rate (RR) calculation block (e.g., RR calculation <NUM>) may be used to take a median respiration rate value as the respiration rate <NUM> output. The RR calculation <NUM> may output the median respiration rate value at a specified time interval (e.g., once per second).

<FIG> is a flow chart illustrating operations of example methods for providing non-contact vital signal monitoring in accordance with some embodiments. It will be understood that the method <NUM> may be performed by a device, such as a computing device executing instructions of a software system. For instance, operations of a method <NUM> may be represented by executable instructions (e.g., software) that, when executed by a processor of a computing device, cause the computing device to perform the method <NUM>. Thus, an operation of the method <NUM> may be performed by a hardware processor (e.g., central processing unit) of a computing device (e.g., desktop computer, server, etc.). Accordingly, the method <NUM> is described below in reference to such a computing device. Depending on the embodiment, an operation of the method <NUM> may be repeated in different ways or involve intervening operations not shown. Though the operations of the method <NUM> may be depicted and described in a certain order, the order in which the operations are performed may vary among embodiments, including performing certain operations in parallel.

Method <NUM> begins at operation <NUM> where a computing device receives a first signal. The first signal is obtained from a first accelerometer channel, extra-corporeally (e.g., without making physical contact with a user).

At operation <NUM>, the computing device receives a second signal. The second signal may be received from a second accelerometer channel, the second signal is also obtained extra-corporeally. The first signal can be obtained by a first accelerometer channel (e.g., measuring a y axis) and the second signal can be obtained by a second accelerometer channel (e.g., measuring an x axis).

In some embodiments, both the first and the second accelerometer channels are channels of a single accelerometer unit. In some embodiments, the single accelerometer unit may have more channels. In some other embodiments, the first and second accelerometer channels may be from separate or distinct accelerometer units. In some embodiments, the computing device receives a third signal measuring the physiologic signal from a third accelerometer channel (e.g., measuring a z axis) where the third accelerometer is distinct from the first and second accelerometer channels. In some embodiments, the first accelerometer channel, the second accelerometer channel, and/or the third accelerometer channels are channels of a solid-state accelerometer. In some embodiments, the first, second, and/or third accelerometer channels define orthogonal sensing axes. For example, the first channel measures a y-axis, the second channel measures an x-axis, and third channel measures a z-axis.

The computing device at operation <NUM> identifies a base frequency using a harmonic template. The computing device may use at least one harmonic template from amongst a plurality of harmonic templates to identify a base frequency of a physiologic signal (e.g., heart rate, respiration rate) from at least one of the first and second signals. In some embodiments, operations <NUM>-<NUM> are performed for at least one of the first and second signals or an aggregated representation of the first and second signals.

In some embodiments, the identified base frequency is constrained to a specified tracking frequency range (e.g., <NUM>-<NUM>).

In response, the computing device applies a first filter to establish a first tracking signal at operation <NUM>. Applying the first filter can include applying a bandpass filter on at least one of the first and second signals or an aggregated representation thereof to generate a first intermediate signal. The first intermediate signal is then reversed, and the bandpass filter is applied on the reversed first intermediate signal to establish the first tracking signal.

In some embodiments, after applying the first filter to establish the tracking signal, the computing device detects that the tracking signal cannot be obtained and sends a notification to an electronic device that the first tracking signal is lost. Detection of the first tracking signal includes monitoring whether a central tendency of noise level values is higher than a current noise level by a specified amount, and in accordance with a determination that the central tendency of noise level values is higher than the current noise level by the specified amount and for a specified duration, declaring the status of the tracking signal as being unavailable. In some embodiments the status of the tracking signal as being unavailable is sent to an electronic device. The tracking signal can be lost or unattainable for various reasons including low signal integrity, high noise, disrupted transmission of the signal, and many more. In some embodiments, the cause of the lost or unattainability of the tracking signal is transmitted to the electronic device.

A second filter is then applied by the computing device at operation <NUM> to establish a second tracking signal. Applying a second filter to establish a second tracking signal can include applying a lowpass filter to generate a second intermediate signal and applying a smoothing filter on the second intermediate signal to establish the second tracking signal.

In some embodiments, the computing device stores the first tracking signal (operation <NUM>) and also stores the second tracking signal (operation <NUM>).

In some embodiments, the first tracking signal is a representation of a heart rate signal. In some embodiments, the first tracking signal represents a heart rate variability signal.

In some embodiments, the second tracking signal represents a respiration rate signal.

In some embodiments, the computing device transmits the first tracking signal (operation <NUM>) and/or transmits the second tracking signal operation <NUM>).

<FIG> is a flow chart illustrating operations of an alternative example method for providing non-contact vital signal monitoring in accordance with some embodiments.

It will be understood that the method <NUM> may be performed by a device, such as a computing device executing instructions of a software system. For instance, operations of a method <NUM> may be represented by executable instructions (e.g., software) that, when executed by a processor of a computing device, cause the computing device to perform the method <NUM>. Thus, an operation of the method <NUM> may be performed by a hardware processor (e.g., central processing unit) of a computing device (e.g., desktop computer, server, etc.). Accordingly, the method <NUM> is described below in reference to such a computing device. Depending on the embodiment, an operation of the method <NUM> may be repeated in different ways or involve intervening operations not shown. Though the operations of the method <NUM> may be depicted and described in a certain order, the order in which the operations are performed may vary among embodiments, including performing certain operations in parallel.

Method <NUM> begins at operation <NUM> where a computing device receives a signal. The signal is obtained from an accelerometer channel, extra-corporeally (e.g., without making physical contact with a user) at a first time window. In some embodiments, the physiologic signal is continuously received by the computing device (e.g., an ongoing signal read). At operation <NUM>, the computing device applies a filter on the physiologic signal to obtain a filtered physiologic signal. In some embodiments, the filter is a bandpass filter. Additionally and/or alternatively, the computing device may apply an equalization operation on the filtered physiologic signal.

At operation <NUM>, the computing device performs a beat time estimation on the filtered physiologic signal within the first time window (e.g., a five second window). In computing the beat time estimation, the computing device may determine peak samples of the filtered physiologic signal and trough samples of the filtered physiologic signal. The computing device then calculates beat times as a function of the peak to trough samples and calculates a median of beat times as being the beat time estimation. Based on the beat time estimation, the computing device can calculate a heart rate (operation <NUM>), a heart rate variability (<NUM>), and a respiration rate (<NUM>). In some embodiments, any of the heart rate, HRV, and respiration rates can be calculated for a specified time segment (e.g., for a <NUM> second time window).

In some embodiments, in calculating the respiration rate (operation <NUM>), the computing device applies a detrending operation to generate a detrended physiologic signal. The computing device then applies a second filter on the detrended physiologic signal to generate a filtered detrended physiologic signal. The second filter (e.g., a low-pass filter) may be distinct from the filter of operation <NUM>. The computing device then calculates peak-to-peak times of the filtered detrended physiologic signal and calculates the respiration rate. In some embodiments, the computing device also calculates trough-to-trough times of the filtered detrended physiologic signal and uses both the peak-to-peak and trough-to-trough times to calculate the respiration rate.

At operation <NUM>, the computing device stores and or transmits the heart rate, heart rate variability, respiration rate, and/or the filtered physiologic signal.

<FIG> is a representation of an example signal graph <NUM> comprising a heart rate and respiration rate signals of the system described herein, according to some embodiments.

An algorithm for determining a tracker signal (e.g., heart rate or respiration rate) includes locking onto and tracking harmonic stacks in the ballistocardiograph's short-time spectrum. In one embodiment, a bank of trackers is instantiated, each tuned to an integer multiple of an estimated fundamental frequency of the tracked signal (e.g., heart rate, respiration rate). Each of the trackers will also have a bandpass filter tuned to the same integer multiple of the estimated fundamental frequency to isolate one harmonic. In another embodiment, a sparse multiplication function is applied to the harmonic stack templates of the trackers to compute the tracking signal around an estimated fundamental frequency at each frame of the ballistocardiograph.

The received data signals from the accelerometers <NUM> are filtered to process out vibration and noise that fall out of the frequency range of the heartbeat and/or respiration rate (e.g., <NUM> to <NUM>). Noise outside of the frequency range is most likely external noise from the user/ patient or environmental noise.

In some embodiments, more than one harmonic of the tracked physiologic signal is used to derive the fundamental frequency (F0) to obtain the tracked physiologic signal representing a user's underlying physiologic activity.

In some embodiments, the received signals are processed using various operations described herein. For example, for overlapping blocks of samples (e.g., <NUM> samples with <NUM>/<NUM> temporal overlap @ <NUM> sampling rate), a spectrum of the block is determined. The computed magnitude may be computed with a fast Fourier transform (FFT). Second, an inner product of the magnitude spectrum is calculated with each of a sparse harmonic comb template to obtain the energy values at each fundamental frequency (F0). Third, update the tracker with the comb energies and fourth, report the current fundamental frequency (F0) estimate and the tracker's status.

In some embodiments, the tracker is established by using alpha-beta filters. Establishing the tracker signal with alpha-beta filters include: performing a prediction using the alpha-beta filter, detecting the tracker by finding the peak near the tracker's center and evaluating the peak quality score (PQS) discussed in further detail below, and updating the status of the tracker if the PQS score is consistently high, with a "tracking" status, if the score is not consistently high, with a "lost" status, and further re-initializing the tracking with a "tentative" status.

In some embodiments, octave errors may be detected in the tracker that may require correcting. In some embodiments, there are occasions when there are not enough harmonics in the frequency-domain signal. To address this, in some embodiments, the tracker is tracked at half of the fundamental frequency (F0) and a peak quality score (PQS) is evaluated. If the calculated PQS for the tracker is higher than a threshold value, the tracked F0 is tracked to the peak of the F0.

In accordance with some embodiments, re-initializing tracking may occur at the beginning of a session, after a noise burst is detected, or when the tracker signal is lost for a predetermined period. Re-initializing the tracking includes initializing multiple alpha-beta filter instances on the largest peaks in the current frame and tentatively tracking each of them for a predetermined number of frames. If one or more of the tentative tracks has a PQS signal that meets a threshold requirement, the track with the highest PQS score is selected and tracked.

In some embodiments, a peak quality score is calculated when tracking the physiologic signal. As in some embodiments, the tracking signal gets lost and is then re-initialized to begin tracking the physiologic signal again. To do so, a peak quality score is calculated. The peak quality score is calculated by evaluating the prominence of the peak closes to the tracked signal. This can be implemented by dividing the peak value by the average of the median values in the windows immediately to the left and to the right of the peak.

As shown, the processed heart rate signal and respiration rate signals <NUM> are devoid of noise and extraneous information. The processed respiration rate signals <NUM> may be sent to an external electronic device (e.g., a physician's computer) for monitoring and/or further analysis.

<FIG> is a block diagram illustrating components of a device <NUM> according to some embodiments, able to read instructions from a machine readable medium (e.g., machine-readable storage medium or machine-readable storage device) and perform any one or more of the methodologies discussed herein. The instructions may cause the device <NUM> to execute the method illustrated in <FIG> and/or <FIG>. Additionally, or alternatively, the instructions <NUM> may implement one or more of the components of <FIG>. The instructions <NUM> transform the general, non-programmed device <NUM> into a particular device <NUM> programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the device <NUM> operates as a standalone device or may be coupled (e.g., connected) to other machines.

The device <NUM> may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a PDA, or any machine capable of executing the instructions <NUM>, sequentially or otherwise, that specify actions to be taken by device <NUM>. Further, while only a single device <NUM> is illustrated, the term "machine" shall also be taken to include a collection of devices that individually or jointly execute the instructions <NUM> to perform any one or more of the methodologies discussed herein.

The device <NUM> may include processors <NUM>, memory/storage <NUM>, and I/O components <NUM>, which may be configured to communicate with each other such as via a bus <NUM>. In an example embodiment, the processors <NUM> (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, processor <NUM> and processor <NUM> that may execute the instructions <NUM>. The term "processor" is intended to include multi-core processor that may comprise two or more independent processors (sometimes referred to as "cores") that may execute instructions <NUM> contemporaneously. Although <FIG> shows multiple processors <NUM>, the device <NUM> may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core process), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

The memory/storage memory <NUM> may include a memory <NUM>, such as a main memory <NUM>, static memory <NUM>, or other memory storage, and a storage unit <NUM>, both accessible to the processor <NUM> such as via the bus <NUM>. The storage unit <NUM> and memory <NUM> store the instructions <NUM> embodying any one or more of the methodologies or functions described herein. The instructions <NUM> may also reside, completely or partially, within the memory <NUM>, within the storage unit <NUM>, within at least one of the processors <NUM> (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the device <NUM>. Accordingly, the memory <NUM>, the storage unit <NUM>, and the memory of processors <NUM> are examples of machine-readable media.

As used herein, "machine-readable medium" includes a machine-readable storage device able to store instructions <NUM> and data temporarily or permanently and may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or any suitable combination thereof. The term "machine-readable medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions <NUM>. The term "machine-readable medium" shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions <NUM>) for execution by a device (e.g., device <NUM>), such that the instructions, when executed by one or more processors of the device <NUM> (e.g., processors <NUM>), cause the device <NUM> to perform any one or more of the methodologies described herein. Accordingly, a "machine-readable medium" refers to a single storage apparatus or device, as well as "cloud-based" storage systems or storage networks that include multiple storage apparatus or devices. The term "machine-readable medium" excludes signals per se.

The input/output (I/O) components <NUM> may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components <NUM> that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components <NUM> may include many other components that are not shown in <FIG>. The I/O components <NUM> are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components <NUM> may include output components <NUM> and input components <NUM>. The output components <NUM> may include audio/ visual components <NUM> (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components <NUM> may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photooptical keyboard, or other alphanumeric input components), point based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio/visual input components <NUM> (e.g., a microphone), and the like.

Communication may be implemented using a wide variety of technologies. The I/O components <NUM> may include communication components <NUM> operable to couple the device <NUM> to a network <NUM> or devices <NUM> via coupling <NUM> and coupling <NUM>, respectively. For example, the communication components <NUM> may include a network interface component or other suitable device to interface with the network <NUM>. In further examples, communication components <NUM> may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices <NUM> may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

For example, the communication components <NUM> may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect onedimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF416, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components <NUM>, such as location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.

The instructions <NUM> may be transmitted or received over the network <NUM> using a transmission medium via a network interface device (e.g., a network interface component included in the communication components <NUM>) and utilizing any one of several well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions <NUM> may be transmitted or received using a transmission medium via the coupling <NUM> (e.g., a peer-to-peer coupling) to devices <NUM>. The term "transmission medium" shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions <NUM> for execution by the device <NUM>, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

Although a few embodiments have been described in detail above, other modifications are possible. For example, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. Other steps may be provided, or steps may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. Other embodiments may be within the scope of the following claims.

This summary is intended to provide an overview of subject matter of the present patent application.

The drawings show, by way of illustration, specific implementations in which the invention can be practiced. These implementations are also referred to generally as "examples.

In this document, the terms "including" and "in which" are used as the plain-English equivalents of the respective terms "comprising" and
"wherein. " Also, in the following aspects, the terms "including" and "comprising" are open-ended, that is, a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such a term in an aspect are still deemed to fall within the scope of that aspect. Moreover, in the following aspects, the terms "first," "second," and "third," etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

Claim 1:
A method, comprising:
receiving a physiologic signal from an accelerometer channel, the signal obtained extra-corporeally at a first time window;
applying a filter on the physiologic signal to obtain a filtered physiologic signal (<NUM>);
performing, on the filtered physiologic signal, a beat time estimation within the first time window (<NUM>), wherein performing the beat time estimation further comprises:
determining peak samples of the filtered physiologic signal;
determining trough samples of the filtered physiologic signal;
calculating beat times as a function of the peak and trough samples; and
calculating a median of beat times as the beat time estimation;
based on the estimation, calculating a heart rate from the filtered physiologic signal (<NUM>); and
at least one of storing or transmitting the heart rate (<NUM>).