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: A method may include collecting sensor data related to a physical or mental health of a patient. The method may include determining a series of physical evidence nodes. The method may include determining a series of physical states of the patient. Each physical state is associated with a single physical evidence node. The method may include determining an expected value for each physical evidence node based on a physical baseline of the patient. The method may include determining, after each day, a value for each corresponding physical evidence node, wherein the value for each physical evidence node includes a value from each associated physical state. The method may include determining, after each day, a deviation of the patient from the expected value. The method may include generating a physical predictive model of the physical health of the patient based on the deviation of the patient from the expected value.

Abstract: A method may include collecting sensor data related to a physical or mental health of a patient. The method may include determining a series of mental evidence nodes. The method may include determining a series of mental states of the patient. Each mental state is associated with a single mental evidence node. The method may include determining an expected value for each mental evidence node based on a mental baseline of the patient. The method may include determining, after each day, a value for each corresponding mental evidence node, wherein the value for each mental evidence node includes a value from each associated mental state. The method may include determining, after each day, a deviation of the patient from the expected value. The method may include generating a mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value.

Abstract: A method may include collecting sensor data related to a set of lifestyle activities related to one or more chronic diseases of a patient. The method may include collecting patient data that indicates at least one of a biological, psychological, or social profile of the patient, and one or more lifestyle choice prescriptions of the patient. The method may include receiving patient input that indicates compliance of the patient with the lifestyle choice prescriptions. The method may include determining a lifestyle choice compliance (LCC) score of the patient based on at least one of the sensor data related to the set of lifestyle activities and the patient input that indicates compliance of the patient with the lifestyle choice prescriptions. The method may include generating an LCC result, wherein the LCC result includes the LCC score of the patient. The method may include providing the LCC result to a care-provider.

Abstract: A method may include collecting sensor data related to a quality of health of a patient. The method may include receiving patient input in response to a minimum number of questions for high statistical measurement of three health dimensions of the patient. The method may include generating a health score for each of three health dimensions of the patient. The method may include categorizing the health score for each of the three health dimensions. The method may include determining a minimum category of questions for high statistical measurement of each category of the three health dimensions. The method may include quantifying each of the three health dimensions of the patient per a VAS health score. The method may include generating a health risk score that is based on the health score for each of the three health dimensions. The method may include providing a health risk result to a care-provider.

Abstract: A method may include collecting sensor data and patient data. The method may include receiving input from a patient that provides a quantification of a present health risk assessment of the patient using minimum clicks in minimum categories using a VAS. The method may include receiving patient input including both manual input and sensor output in response to one or more questions directed to a quality of health of the patient. The method may include generating a CCB score with a pre-defined risk stratification. The CCB score may be based on at least two of the patient data, the sensor data, and the patient input. The method may include generating a CCB result. The CCB result may include the CCB score and a risk stratification of the patient based on the pre-defined risk stratification and the CCB score. The method may include providing a CCB result to a care-provider.

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: A system for evaluating health-related quality of life (HRQOL) can include a sensor adapted to generate sensor data for a user, a memory adapted to store the sensor data, and a processor coupled to the sensor and the memory. The processor can be configured to initiate executable operations including determining a first biological marker for the user from the sensor data, wherein the first biological marker is indicative of a first dimension of HRQOL, performing speech analysis on user provided speech to determine a second biological marker indicative of a second dimension of HRQOL. The processor is further configured to initiate executable operations including comparing the first biological marker and the second biological marker with a baseline for the first biological marker and a baseline for the second biological marker, respectively, and outputting a result of the comparing.

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: Chronotherapeutic dosing can include receiving, using a processor, sensor data from a sensor for a user, wherein the sensor data is collected subsequent to the user starting a regimen for a medication, determining, using the processor, a biological marker from the sensor data, wherein the biological marker is correlated with the medication, and comparing, using the processor, the biological marker with an expected state of the biological marker based upon a dose time of the medication. Chronotherapeutic dosing can also include providing, using the processor, a notification indicating a result of the comparing.

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: 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: 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 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: 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, 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: A method may include collecting data indicating a number of patients diagnosed with one or more chronic diseases in a geographic location based on epidemiological attributes of the patients. The method may include collecting data that indicates a number of patients that are diagnosed with the chronic diseases in a practice of a physician. The method may include comparing the number of the patients diagnosed with the chronic diseases in the practice of the physician to the number of patients diagnosed with the chronic diseases in the geographic location. The method may include generating a comparison result that may indicate whether the number of patients diagnosed with the chronic diseases in the practice of the physician is greater than or less than the number of patients diagnosed with the chronic diseases in the geographic location. The method may include providing an alert to a care-provider that includes the comparison result.

Abstract: A method may include collecting sensor data and patient data. The method may include receiving input from a patient that provides a quantification of a present health risk assessment of the patient using minimum clicks in minimum categories using a VAS. The method may include receiving patient input including both manual input and sensor output in response to one or more questions directed to a quality of health of the patient. The method may include generating a CCB score with a pre-defined risk stratification. The CCB score may be based on at least two of the patient data, the sensor data, and the patient input. The method may include generating a CCB result. The CCB result may include the CCB score and a risk stratification of the patient based on the pre-defined risk stratification and the CCB score. The method may include providing a CCB result to a care-provider.

Abstract: A method may include collecting sensor data related to a quality of health of a patient. The method may include receiving patient input in response to a minimum number of questions for high statistical measurement of three health dimensions of the patient. The method may include generating a health score for each of three health dimensions of the patient. The method may include categorizing the health score for each of the three health dimensions. The method may include determining a minimum category of questions for high statistical measurement of each category of the three health dimensions. The method may include quantifying each of the three health dimensions of the patient per a VAS health score. The method may include generating a health risk score that is based on the health score for each of the three health dimensions. The method may include providing a health risk result to a care-provider.

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.