Abstract: A method includes obtaining a baseline blood pressure at an initial time window; estimating a plurality of intermediate blood pressure change estimates, the intermediate blood pressure change estimates correspond to respective time windows that are subsequent to the initial time window; estimating a final blood pressure change estimate between the initial and final time windows; and obtaining the blood pressure by adding the baseline blood pressure to the final blood pressure change estimate. Estimating the final blood pressure includes estimating a first blood pressure change between the initial time window and the final time window; estimating a plurality of second blood pressure changes, each second blood pressure change is between a respective time window of the respective time windows and the final time window; and estimating the final blood pressure change estimate as a combination of the first blood pressure change and the plurality of second blood pressure changes.

Abstract: A wearable device includes a processor and a lower module. The lower module includes a pressure sensor for detecting a mechanical movement of a skin that covers an artery. The mechanical movement of the skin is due to blood flow through the artery. The processor is configured to receive skin movement information from the movement sensor; calculate a pulse front velocity (PFV), which is a velocity of a blood wave as the blood wave passes under the pressure sensor; estimate a pulse wave velocity (PWV) using the PFV; and estimate the blood pressure using the PWV.

Abstract: Described herein are systems and methods for collecting physiological information from a user and determining information including, but not limited to, a user's heart rate, blood pressure, oxygen levels (SvO2), hydration, respiration rate, and heart rate variability. Physiological information is collected from one or more modules, each comprising a sensor array. The sensor array(s) can comprise, among other things, light sources, photo detectors, ECG electrodes/sensors, bio impedance sensors, galvanic skin response sensors, tonometry/contact sensors, accelerometers, pressure sensors, acoustic sensors, and electromagnetic sensors. The information collected from a user can be used to cross-reference a database comprising similar information for a number of subjects as well as verified measurements for each subject including, but not limited to, blood pressure, oxygen levels (SvO2), hydration, respiration rate, and heart rate variability.

Abstract: A portable device for measuring an electrocardiogram (ECG) of a user. The portable device includes a first ECG sensor that is configured to touch a body of the user, and a processor. The processor is configured to receive a first ECG measurement at a first location on the body of the user, where the first ECG sensor is placed at the first location of the body; identify a first region of the heart of the user based on the first location; direct the user to move the portable device to a second location on the body of the user; receive a second ECG measurement at the second location on the body; and identify a second region of the heart of the user based on the second location.

Abstract: Measuring, using a wearable device, a blood pressure of a user includes extracting, using sensor data of the wearable device, features related to a pulse wave; determining a pulse transit time (PTT); scaling at least one of the features using the PTT to obtain a scaled feature; using the scaled feature as an input to a machine-learning (ML) model; and obtaining, using an output of the ML model, the blood pressure of the user.

Abstract: A method includes obtaining a baseline blood pressure at an initial time window; estimating a plurality of intermediate blood pressure change estimates, the intermediate blood pressure change estimates correspond to respective time windows that are subsequent to the initial time window; estimating a final blood pressure change estimate between the initial and final time windows; and obtaining the blood pressure by adding the baseline blood pressure to the final blood pressure change estimate. Estimating the final blood pressure includes estimating a first blood pressure change between the initial time window and the final time window; estimating a plurality of second blood pressure changes, each second blood pressure change is between a respective time window of the respective time windows and the final time window; and estimating the final blood pressure change estimate as a combination of the first blood pressure change and the plurality of second blood pressure changes.

Abstract: Physiological signals produced at a wearable device are compressed and mapped to detect a health condition of a user of the wearable device. Physiological signal data indicating a physical quality of the user of the wearable device is produced based on noisy data recorded using sensors of the wearable device. The physiological signal data is compressed at the wearable device using a dictionary defined at a server device. The server device receives and decompresses the compressed physiological signal data to produce denoised physiological signal data indicating the physical quality of the user of the wearable device. A change in a physiological state of the user of the wearable device is determined using the denoised physiological signal data and historical physiological data of the user of the wearable device. The health condition is detected based on the change in the physiological state of the user of the wearable device.

Abstract: A wearable device includes a processor and a lower module. The lower module includes a pressure sensor for detecting a mechanical movement of a skin that covers an artery. The mechanical movement of the skin is due to blood flow through the artery. The processor is configured to receive skin movement information from the movement sensor; calculate a pulse front velocity (PFV), which is a velocity of a blood wave as the blood wave passes under the pressure sensor; estimate a pulse wave velocity (PWV) using the PFV; and estimate the blood pressure using the PWV.

Abstract: Described herein are systems and methods for collecting physiological information from a user and determining information including, but not limited to, a user's heart rate, blood pressure, oxygen levels (SvO2), hydration, respiration rate, and heart rate variability. Physiological information is collected from one or more modules, each comprising a sensor array. The sensor array(s) can comprise, among other things, light sources, photo detectors, ECG electrodes/sensors, bio impedance sensors, galvanic skin response sensors, tonometry/contact sensors, accelerometers, pressure sensors, acoustic sensors, and electromagnetic sensors. The information collected from a user can be used to cross-reference a database comprising similar information for a number of subjects as well as verified measurements for each subject including, but not limited to, blood pressure, oxygen levels (SvO2), hydration, respiration rate, and heart rate variability.

Abstract: A system for determining a life threatening condition of a human may include at least one mobility sensor couplable to a human body and configured to measure mobility parameters of said human body; at least one physiological sensor couplable to a human body and configured to measure physiological parameters of said human body; at least one environmental sensor configured to measure environmental parameters indicative of an environment of said human body; and a computer processor configured to apply a decision function configured to determine a predefined life endangering condition of said human body, based on said measured mobility, physiological, and environmental parameters, and statistical relationship therebetween.

Abstract: A system for determining a life threatening condition of a human may include at least one mobility sensor couplable to a human body and configured to measure mobility parameters of said human body; at least one physiological sensor couplable to a human body and configured to measure physiological parameters of said human body; at least one environmental sensor configured to measure environmental parameters indicative of an environment of said human body; and a computer processor configured to apply a decision function configured to determine a predefined life endangering condition of said human body, based on said measured mobility, physiological, and environmental parameters, and statistical relationship therebetween.

Abstract: A Photoplethysmography-based sensor for measuring heart rate is provided herein. The sensor may include a first light source and a second light source configured to illuminate a body tissue by a first light and a second light respectively; and a first and a second light detectors, each configured to detect light comprising portions of said first light and of said second light, transferred through the body tissue; and a processor with an analog measurement part configured to: receive light intensity readings of at least a portion of light as sensed by each one of both sensors and coming from each one of both sources; and calculate a measure of tissue absorption based on ratios of light portions transmitted by each one of both sources and measured by each one of both detectors.