Abstract: A physiological signal processing system for a physiological waveform that includes a cardiovascular signal component provides a variable high pass filter that is responsive to the physiological waveform, and that is configured to high pass filter the physiological waveform in response to a corner frequency that is applied. A heart rate metric extractor is responsive to the variable high pass filter and is configured to extract a heart rate metric from the physiological waveform that is high pass filtered. A corner frequency adjuster is responsive to the heart rate metric extractor and is configured to determine the corner frequency that is applied to the variable high pass filter, based on the heart rate metric that was extracted. Analogous methods may also be provided.

Abstract: A wearable device collects a plurality of photoplethysmography (PPG) waveforms from a PPG sensor in the wearable device and collects inertial data associated with subject motion from an inertial sensor in the wearable device. The wearable device processes the inertial data in an assessment processor of the wearable device to determine a data integrity of the plurality of PPG waveforms and, responsive to the determined data integrity, processes the plurality of PPG waveforms in the assessment processor using a neural network comprising thousands of coefficients to generate an assessment of the subject blood pressure.

Abstract: The method and apparatus disclosed herein determine a user cadence from the output of an inertial sensor mounted to or proximate the user's body. In general, the disclosed cadence measurement system determines the user cadence based on frequency measurements acquired from an inertial signal output by the inertial sensor. More particularly, a cadence measurement system determines a user cadence from an inertial signal generated by an inertial sensor, where the inertial signal comprises one or more frequency components. The cadence measurement system determines a peak frequency of the inertial signal, where the peak frequency corresponds to the frequency component of the inertial signal having the largest amplitude. After applying the peak frequency to one or more frequency threshold comparisons, the cadence measurement system determines the user cadence based on the peak frequency and the frequency threshold comparison(s).

Abstract: Methods and apparatus disclosed herein use a filtering technique to improve the accuracy of the results achieved when processing data provided by a physiological sensor. The disclosed filtering technique corrects many of the accuracy problems associated with physiological sensors, particularly PPG sensors. Broadly, the filtering technique adjusts a current filtered estimate of a physiological metric as a function of a rate limit based on a comparison between an instantaneous estimate of the physiological metric and the current filtered estimate.

Abstract: A headset includes a housing defining an audio cavity, a speaker located within the audio cavity, and first and second sensor modules within the housing in spaced-apart, angled relationship to each other. The housing includes an aperture through which sound from the speaker can pass, and the first and second sensor modules are on opposing sides of a direction from the speaker to the aperture. The first sensor module is configured to direct electromagnetic radiation at a first target region of an ear of a person wearing the headset and to detect a first energy response signal therefrom that is associated with one or more physiological metrics of the subject, and the second sensor module is configured to direct electromagnetic radiation at a second target region of the ear and to detect a second energy response signal therefrom that is associated with the one or more physiological metrics.

Abstract: A wearable device collects a plurality of photoplethysmography (PPG) waveforms from a PPG sensor in the wearable device and collects inertial data associated with subject motion from an inertial sensor in the wearable device. The wearable device processes the inertial data in an assessment processor of the wearable device to determine a data integrity of the plurality of PPG waveforms and, responsive to the determined data integrity, processes the plurality of PPG waveforms in the assessment processor using a neural network comprising thousands of coefficients to generate an assessment of the subject blood pressure.

Abstract: Physiological signal processing systems include a photoplethysmograph (PPG) sensor that is configured to generate a physiological waveform, and an inertial sensor that is configured to generate a motion signal. A physiological metric extractor is configured to extract a physiological metric from the physiological waveform that is generated by the PPG sensor. The physiological metric extractor includes an averager that has an impulse response that is responsive to the strength of the motion signal. Related methods are also described.

Abstract: The methods and apparatuses presented herein determine and/or improve the quality of one or more physiological assessment parameters, e.g., response-recovery rate, based on biometric signal(s) and/or motion signal(s) respectively output by one or more biometric and/or motion sensors. The disclosed methods and apparatuses also estimate a user's stride length based on a motion signal and a determined type of user motion, e.g., walking or running. The speed of the user may then be estimated based on the estimated stride length.

Abstract: A wearable device includes a housing, at least one optical emitter supported by the housing, wherein the at least one optical emitter is configured to generate modulated light, at least one optical detector supported by the housing, and a tip removably secured to the housing. The tip includes first and second portions, wherein the first portion directs light along a first path from the at least one optical emitter to a body of a subject wearing the device, and wherein the second portion directs light from the body of the subject to the optical detector along a second path that is different from the first path. The device may also include an analog-to-digital converter, a motion sensor, and a processor. The processor executes a first filter that attenuates sunlight noise and a second filter that attenuates motion artifacts.

Abstract: The methods and apparatuses presented herein determine and/or improve the quality of one or more physiological assessment parameters, e.g., response-recovery rate, based on biometric signal(s) and/or motion signal(s) respectively output by one or more biometric and/or motion sensors. The disclosed methods and apparatuses also estimate a user's stride length based on a motion signal and a determined type of user motion, e.g., walking or running. The speed of the user may then be estimated based on the estimated stride length.

Abstract: The heart rate monitor disclosed herein removes a step rate component from a measured heart rate by using one or more filtering techniques when the step rate is close to the heart rate. In general, a difference between the step rate and the heart rate is determined, and the step rate is filtered from the heart rate based on a function of the difference.

Abstract: The heart rate monitor disclosed herein removes a step rate component from a measured heart rate by using one or more filtering techniques when the step rate is close to the heart rate. In general, a difference between the step rate and the heart rate is determined, and the step rate is filtered from the heart rate based on a function of the difference.

Abstract: A physiological signal processing system for a physiological waveform that includes a cardiovascular signal component provides a variable high pass filter that is responsive to the physiological waveform, and that is configured to high pass filter the physiological waveform in response to a corner frequency that is applied. A heart rate metric extractor is responsive to the variable high pass filter and is configured to extract a heart rate metric from the physiological waveform that is high pass filtered. A corner frequency adjuster is responsive to the heart rate metric extractor and is configured to determine the corner frequency that is applied to the variable high pass filter, based on the heart rate metric that was extracted. Analogous methods may also be provided.

Abstract: The heart rate monitor disclosed herein removes a step rate component from a measured heart rate by using one or more filtering techniques when the step rate is close to the heart rate. In general, a difference between the step rate and the heart rate is determined, and the step rate is filtered from the heart rate based on a function of the difference.

Abstract: A physiological signal processing system for a physiological waveform that includes a cardiovascular signal component provides a variable high pass filter that is responsive to the physiological waveform, and that is configured to high pass filter the physiological waveform in response to a corner frequency that is applied. A heart rate metric extractor is responsive to the variable high pass filter and is configured to extract a heart rate metric from the physiological waveform that is high pass filtered. A corner frequency adjuster is responsive to the heart rate metric extractor and is configured to determine the corner frequency that is applied to the variable high pass filter, based on the heart rate metric that was extracted. Analogous methods may also be provided.

Abstract: The methods and apparatuses presented herein determine and/or improve the quality of one or more physiological assessment parameters, e.g., response-recovery rate, based on biometric signal(s) and/or motion signal(s) respectively output by one or more biometric and/or motion sensors. The disclosed methods and apparatuses also estimate a user's stride length based on a motion signal and a determined type of user motion, e.g., walking or running. The speed of the user may then be estimated based on the estimated stride length.

Abstract: The methods and apparatuses presented herein determine and/or improve the quality of one or more physiological assessment parameters, e.g., response-recovery rate, based on biometric signal(s) and/or motion signal(s) respectively output by one or more biometric and/or motion sensors. The disclosed methods and apparatuses also estimate a user's stride length based on a motion signal and a determined type of user motion, e.g., walking or running. The speed of the user may then be estimated based on the estimated stride length.

Abstract: Methods and apparatus disclosed herein use a filtering technique to improve the accuracy of the results achieved when processing data provided by a physiological sensor. The disclosed filtering technique corrects many of the accuracy problems associated with physiological sensors, particularly PPG sensors. Broadly, the filtering technique adjusts a current filtered estimate of a physiological metric as a function of a rate limit based on a comparison between an instantaneous estimate of the physiological metric and the current filtered estimate.

Abstract: The heart rate monitor disclosed herein removes a step rate component from a measured heart rate by using one or more filtering techniques when the step rate is close to the heart rate. In general, a difference between the step rate and the heart rate is determined, and the step rate is filtered from the heart rate based on a function of the difference.

Abstract: The methods and apparatuses presented herein determine and/or improve the quality of one or more physiological assessment parameters, e.g., response-recovery rate, based on biometric signal(s) and/or motion signal(s) respectively output by one or more biometric and/or motion sensors. The disclosed methods and apparatuses also estimate a user's stride length based on a motion signal and a determined type of user motion, e.g., walking or running. The speed of the user may then be estimated based on the estimated stride length.