One embodiment provides a method, including: receiving movement data describing physical movement of a person performing a predetermined action; generating, using a processor, classification of the movement data using a test application that predicts output of a wearable device, wherein the test application has been formed using previously collected data that describe the movement of a person performing the predetermined action; determining, using the processor, whether the movement data match the predetermined action in view of the classification; receiving output of a body-aware application that detects and responds to human movement; comparing, using the processor, the output of the body-aware application with the classification; and providing, using the processor, an indication of the comparing of the output of the body-aware application and the classification.

During a first time period and for a first user, a second user is automatically selected based on competitive data of the first user and competitive data of the second user, and a workout selection is sent to cause a video of a workout to be displayed during a second time period on a smart mirror of the first user and a smart mirror of the second user. During the second time period, a live stream of the first user exercising is displayed at the smart mirror of the second user, and a live stream of the second user exercising is received and displayed at the smart mirror of the first user. During the second time period, a performance score of the first user and a performance score of the second user is displayed at the smart mirrors of the first user and the second user.

A computer-implemented method of searching for a region indicative of a pathology in an image of a portion of an eye acquired by an ocular imaging system, the method comprising: receiving image data defining the image; searching for the region in the image by processing the received image data using a learning algorithm; and in case a region in the image that is indicative of the pathology is found: determining a location of the region in the image; generating an instruction for an eye measurement apparatus to perform a measurement on the portion of the eye to generate measurement data, using a reference point based on the determined location for setting a location of the measurement on the portion of the eye; and receiving the measurement data from the eye measurement apparatus.

A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects. Correction actors, fusion algorithms, EIS, and advanced ASICs may be used to implement the foregoing, thereby achieving the goal of improved accuracy and reliability without the need for blood-glucose calibration, and providing a calibration-free, or near calibration-free, sensor.

An electronic device, a method to be executed by an electronic device, and a non-transitory memory storing a program for causing an electronic device to execute processes include acquiring a pulse wave of a subject, and estimating a blood glucose level and/or a lipid level of the subject based on a displacement ratio in the pulse wave. The displacement ratio comprises a ratio between a displacement of the pulse wave at a peak of the pulse wave and a displacement of the pulse wave at a predetermined time after the peak of the pulse wave, and the predetermined time is a fixed value.

An estimation apparatus is configured to be capable of accessing a model storage unit that stores a model built by machine learning, using, as training data, information on a predetermined sound and information relating to a signal source of a signal indicating a brain activity of a first subject presented with the predetermined sound, the model outputting information on a sound estimated to be recognized by the subject. The estimation apparatus acquires a brain wave of a second subject presented with the predetermined sound. The estimation apparatus estimates, based on a mode of the brain wave acquired, a signal source of the brain wave, from among a plurality of regions in a brain of the second subject. The estimation apparatus inputs the information relating to the signal source estimated to the model and acquires information on a sound estimated to be recognized by the second subject.

Disclosure is a hearing threshold and/or hearing state detection system and method. The system comprises: an acquisition and transmission system configured to transmit stimulation signals and acquire an ear canal signal; and a hearing threshold analysis and prediction system including a hearing threshold detection module, a routine testing module and/or a hearing state screening module, wherein the hearing threshold detection module determines hearing thresholds at different stimulation frequencies through a pre-trained network model; the routine testing module adaptively selects a range of test intensities through the acquisition and transmission system, and predicts hearing thresholds related to different stimulation frequencies through a pre-trained network model; and the screening module is configured to perform hearing state screening through the acquisition and transmission system and a pre-trained network model. A detection result thereof is not only accurate, but also is applicable to various demand scenarios.

Systems and methods for measuring vitals in accordance with embodiments of the invention are illustrated. One embodiment includes a method for measuring vital signs. The method includes steps for identifying regions of interest (ROIs) from video data of an individual, generating temporal waveforms from the ROIs, analyzing the generated temporal waveforms to extract vital sign measurements, and generating outputs based on the analyzed temporal waveforms.

A closed-loop implantable neurostimulator system for mitigating chronic pain, the closed-loop implantable neurostimulator system including a neuromodulation device comprising one or more electrodes configured to measure a physiological signal of a subject and deliver an electrical stimulation signal to a target area in the subject and a controller, in communication with the one or more electrodes, comprising a processor and a computer-readable memory storing a trained healthy computer model, the controller configured to analyze the physiological signal that is measured using the trained healthy computer model to identify a corrective electrical stimulation signal that, when delivered by the one or more electrodes to the target area, reduces pathological neuronal events in the target area while preserving acute pain response.

A system and method includes (i) providing patient data stored in a data repository, (ii) applying a first patient identification algorithm to the patient data to identify an initial group of individuals associated with select physical and health characteristics, (iii) applying a second patient identification algorithm to the patient data associated with the initial group of individuals to identify a narrower subgroup associated with select behavioral characteristics, and (iv) generating patient identifiable information from the patient data to allow for notification. The identification of the initial group is based on a determined likelihood of obstructive sleep apnea (OSA) for individuals meeting or exceeding a first threshold criteria. The identification of the narrower group is based on a determined likelihood of long-term adherence to OSA treatment for individuals meeting or exceeding a second threshold criteria. The notification is of designated entities that one or more of the individuals in the narrower subgroup are preferred individuals for OSA.