Abstract: A method for monitoring a health characteristic of a user based on one or more biological measurements may include selecting a context from a plurality of contexts, each context corresponding to a baseline health value, and each context being defined by a plurality of recorded events each comprising one or more of repeated biological states, repeated user activity, or space-time coordinates of the user, and then monitoring the health characteristic of the user based on one or more bio-sensing measurements in comparison to the baseline health value corresponding to the selected context.

Abstract: In one embodiment, a health-monitoring system may access a waist-hip measurement of a user. The system may determine one or more stress-related parameters of the user using one or more computing devices. The system may determine one or more correlations between the waist-hip measurement and the one or more stress-related parameters of the user. The system may provide feedback to the user based on one or more of the one or more stress-related parameters or the determined correlations between the waist-hip measurement and the one or more stress-related parameters.

Abstract: In particular embodiments, a method for determining a heart-rate variability (HRV) measurement of a user may include receiving, from a sensor of an electronic device of the user, heart sensor data, determining a noise level of the heart sensor data, and then adjusting the HRV measurement by an amount calculated based on the determined noise level. The sensor data may include data on a photoplethysmograph (PPG) signal over a noise-calculation time period.

Abstract: Detecting chronotropic incompetence includes determining, using a processor, a baseline cardiac health measure for a user based upon an estimate of activity level for the user, validating, using the processor, the estimate of activity level for the user with a validation factor determined from sensor data and determining, using the processor, a cardiac health measure for the user from the sensor data. A signal indicating chronotropic incompetence for the user can be provided by the processor based upon a comparison of the cardiac health measure for the user with the baseline cardiac health measure.

Abstract: A method for monitoring a stress level of a user may include determining, based on biological measurements on one or more physiological markers received in a first sampling mode, the stress level of the user, and then dynamically switching to a second sampling mode when it is determined that one or more of the physiological markers are above a threshold level, the second sampling mode being different from the first sampling mode.

Abstract: Monitoring health status of a user may be performed by an apparatus that includes a sensor adapted to generate sensor data, a memory adapted to store a plurality of baseline biological markers for a user, wherein the plurality of baseline biological markers include respiratory rhythm, and a processor coupled to the memory and the sensor. The processor is adapted to, in response to detecting that the user is performing physical activity, determine a biological marker from the sensor data. The processor is adapted to compare the biological marker with the baseline biological marker. The processor is further adapted to, in response to detecting a change in the biological marker, provide a notification indicating a change in health status.

Abstract: A method may include collecting sensor data related to health of a patient and receiving input that provides quantification of health of the patient. The method may include generating a first marker based on the sensor data and/or the quantification of the health of the patient including a first dimension indicative of additional examination to be performed. The method may include generating a second marker based on one or more risk algorithms including a second dimension indicative of an acute assessment of issues related to the health of the patient. The method may include comparing the first and/or second markers to a first or second marker baseline. The method may include generating a health result based on the comparison of the first and/or second marker to the first or second marker baseline and providing an alert to a care-provider that includes the health result.

Abstract: In one embodiment, one or more computer-readable non-transitory storage media embody software that is operable when executed to determine, based on cardiac-activity data, a shift in sympathovagal balance (SVB) during the period of time; obtain an emotional-state characteristic the emotional-state characteristic being based on brain-wave activity data; and determine, based on the SVB shift and the emotional-state characteristic, an estimate of an emotional state of the user during the period of time.

Abstract: A method for determining a stress level of a user based on a sympathovagal balance (SVB) value calculated based on a set of measurement data may include determining a heart-rate variability (HRV) characteristic as a ratio involving a number of autonomic nervous system (ANS) activity markers within a first portion of the set of measurement data and the number of ANS activity markers within a second portion of the set of measurement data, and then determining the stress level of the user based on the HRV characteristic. The first and second portions of the set of measurement data may be selected based on a user-specific baseline SVB value that divides a histogram representation of the set of measurement data into the first and second portions.

Abstract: An apparatus for assessing heart failure can include an image sensor configured to capture image data of a patient, a sensor configured to capture sensor data for the patient, a memory configured to store the image data and the sensor data, and a processor coupled to the image sensor, the sensor, and the memory. The processor is configured to receive image data in response to detecting a biological condition from the sensor data, wherein the biological condition is indicative of psychophysiological health and cardiac health. The processor is further configured to detect a visual characteristic from the image data, wherein the visual characteristic is indicative of heart health, and, in response to detecting the visual characteristic, provide an indication that the patient is experiencing a worsening of heart failure.

Abstract: Evaluating health of a user can include providing an equilibrium envelope for a user, wherein the equilibrium envelope includes a range for each of a plurality of vital signs, and detecting, using a processor, a current metric. The current metric corresponds to an exit condition for the equilibrium envelope determined based upon sensor data or a re-entry condition for the equilibrium envelope determined based upon the sensor data. A health status for the user can be determined, using the processor, based upon a comparison of the current metric with a historical metric for the user.

Abstract: Processing PPG data using a device can include determining a quality estimate for a segment of PPG data and, in response to determining that the quality estimate exceeds a quality threshold, filtering the segment of the PPG data based upon an estimate of periodicity of the segment of the PPG data. A health marker can be determined for the segment of PPG data. The health marker can be validated based upon a prior determination of the health marker from PPG data. In response to the validation, the health marker can be output.

Abstract: In one embodiment, a computer-readable non-transitory storage medium embodies software that is operable when executed to, in real time, capture a number of images of a user; and determine a time-series signal for the user based on the plurality of images. The signal includes one or more segments that are physiologically plausible and one or more segments that are physiologically implausible. The software is further operable to identify one or more of the physiologically plausible sub-segments based on one or more pre-defined signal characteristics; and calculate one or more heartrate measurements based on the physiologically plausible sub-segments.

Abstract: In one embodiment, a computer-readable non-transitory storage medium embodies software that is operable when executed to, in real time, capture, by a single sensor, a number of images of a user; determine, based on the number of images, one or more short-term cardiological signals of the user during a period of time; estimate, based on the cardiological signals, a first short-term emotional state of the user; determine, based on the number of images, one or more short-term neurological signals of the user during the period of time; estimate, based on the neurological signals, a second short-term emotional state of the user; compare the first estimated emotional state to the second estimated emotional state; and in response to a determination that the first estimated emotional state corresponds to the second estimated emotional state, determine a short-term emotion of the user during the period of time.

Abstract: In one embodiment, a computer-readable non-transitory storage medium embodies software that is operable when executed to, in real time, capture a number of images of a user; identify one or more regions of interest corresponding to one or more superficial arteries of the user. The identification is based on a signal-to-noise ratio of photoplethysmogram (PPG) data obtained from the plurality of images. The software is further operable to measure, based on the PPG data, blood volume pulse (BVP) signals; and based on the measured BVP signals, compute one or more cardiological metrics for the user.

Abstract: Blood flow of a user can be measured using a sensor. Sensor data based on the measuring of the blood flow can be generated. Based on the sensor data, at least a first physiological biomarker of the blood flow measured by the sensor and at least a first morphological characteristic of the blood flow measured by the sensor can be determined. The user can be authenticated based, at least in part, on the first physiological biomarker and the first morphological characteristic.

Abstract: Monitoring health status of a user may be performed by an apparatus that includes a sensor adapted to generate sensor data, a memory adapted to store a plurality of baseline biological markers for a user, wherein the plurality of baseline biological markers include respiratory rhythm, and a processor coupled to the memory and the sensor. The processor is adapted to, in response to detecting that the user is performing physical activity, determine a biological marker from the sensor data. The processor is adapted to compare the biological marker with the baseline biological marker. The processor is further adapted to, in response to detecting a change in the biological marker, provide a notification indicating a change in health status.

Abstract: Detecting chronotropic incompetence includes determining, using a processor, a baseline cardiac health measure for a user based upon an estimate of activity level for the user, validating, using the processor, the estimate of activity level for the user with a validation factor determined from sensor data and determining, using the processor, a cardiac health measure for the user from the sensor data. A signal indicating chronotropic incompetence for the user can be provided by the processor based upon a comparison of the cardiac health measure for the user with the baseline cardiac health measure.

Abstract: Detecting depression may include generating, using a sensor, sensor data for a user and automatically detecting, using a processor, a marker for depression in the sensor data. Responsive to determining, using the processor, that a condition is satisfied based upon the marker for depression, a survey is presented using a device.

Abstract: In one embodiment, a method includes accessing first time-series data based on electromagnetic radiation in a first spectrum and second time-series data based on electromagnetic radiation in a second spectrum. The method also includes comparing the first time-series data with the second time-series data and determining, based on the comparison, (1) whether a stopping condition associated with a device has occurred or (2) whether a discarding condition associated with the first time-series data or the second time-series data has occurred.