Fujitsu Corporation

Samsung Electronics Co., Ltd.

Other

Fijutsu Limited

Jawahar Jain

Stergios Stergiou

David Loren Marvit

Sajid Hassan Sadi

Cody Blair Wortham

James Young

Pranav K Mistry

B Thomas Adler

Debashis Sahoo

Subramanian K Iyer

Rajarshi Mukherjee

Amit Narayan

Yannis Labrou

Albert H M Reinhardt

Rajalakshmi Balakrishnan

Alexander Gilman

Alex Gilman

Madan Bahadur

Mukul Ranjan Prasad

John J Sidorowich

Shreyans Gandhi

Koichiro Takayama

Vishal Agarwal

Ayesha Mudassir Khwaja

Lakshya Jain

Christian Stangier

Saurabh Tara

Albert C Braun

Abhijit Z Bendale

Vamsi Boppana

Thomas W Sidle

Yoshihisa Kojima

Ajay Chander

Tsuneo Nakata

Fannie Fontanel

Vatche A Attarian

Albert Reinhardt

Michael S Hsiao

Siddharth

Takaya Ogawa

Kelvin K C Ng

Masahiro Fujita

Yuan Lu

Yasunori Kimura

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.

Bio-sensing based monitoring of health

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.

Health risk score for risk stratification and compliance monitoring for next best action

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.

Next best action based by quantifying chronic disease burden on a patient and their willingness to take that action

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.

Sensor assisted evaluation of health and rehabilitation

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.

Real time authentication based on blood flow parameters

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.

Chronotherapeutic dosing of medication and medication regimen adherence

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.

Determining health markers using portable devices

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.

Determining a health status for a user

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.

Determining artery location using camera-based sensing

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.

Determining cardiovascular features using camera-based sensing

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.

Graded escalation based triage

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.

Determining emotions using camera-based sensing

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.

Detecting live tissues using signal analysis

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.

Determining baseline contexts and stress coping capacity

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.

Performance of biological measurements in the presence of noise

Growing community of inventors

Los Altos, CA, United States of America

Jawahar Jain