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.

Methods, systems, and computer-program products used for assessing athletic ability and generating performance data. In one embodiment, athlete performance data is generated through computer-vision analysis of video of an athletic performing, e.g., during practice or gameplay. The generated performance data for the athlete may include, for example, maximum speed, maximum acceleration, time to maximum speed, transition time (e.g., time to change direction), closing speed (e.g., time to close the distance to another athlete), average separation (e.g., between the athlete and another athlete), play-making ability, athleticism (e.g., a weighted computation and/or combination of multiple metrics), and/or other performance data. This performance data may be used to generate and/or update a profile associated with the athlete, which can be utilized for recruiting, scouting, comparing, and/or assessing athletes with greater efficiency and precision.

An exercise form analysis and feedback system (EFAF) including at least one sensor, at least one local movement data receiver, an analysis and feedback processing unit (AFPU) and a feedback display. The EFAF system of the present invention obtains lift movement data through the one or more sensors as lift movements are performed. This lift movement data may, in turn, be transmitted to one or more local movement data receivers such that the AFPU may operate on the lift movement data to provide real-time or near real-time form/technique feedback to the user via a feedback display. The system of the presentation invention uses machine learning techniques to provide feedback on lift quality aspects based on data associated with previous lifts and external data as applicable.

A system includes a wearable sensor device including an accelerometer configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) a lift rate, (b) a maximum sagittal flexion, (c) an average twist velocity, (d) a maximum moment, and (e) a maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.

The various embodiments of the present invention provide a system and method for a fully mobile, non-invasive, continuous system for monitoring the cardiovascular and musculoskeletal health of an individual during exercise, and for administering a protocol for ischemic pre-conditioning. The system includes a wearable devices affixed on the user with a chest strap, coupled with an application running on a computing device (smartphone/smartwatch), which performs various computations on the wearable device, and allows the user to get real time alerts during exercise, by way of vibrations or audio messages or notifications on the gateway device, to guide them through a protocol for ischemic pre-conditioning.

A wearable device for monitoring physical activity of a user, the wearable device being reversibly attachable to a chest strap and an arm strap. The wearable device comprises an ECG sensor arranged to collect ECG measurements of the user only when the wearable device is attached to the user's chest by the chest strap, and a PPG sensor arranged to collect PPG measurements of the user when the wearable device is attached to the user's arm or wrist by the arm strap. The wearable device also comprises control circuitry arranged to switch the wearable device between an ECG only mode and a PPG only mode. In the ECG only mode, the control circuitry is configured to control the ECG sensor to collect ECG measurements but prevent the PPG sensor from initiating PPG measurements. In the PPG only mode, the control circuitry is configured to control the PPG sensor to initiate PPG measurements but prevent the ECG sensor from collecting ECG measurements.

There is provided a method for determining dementia risk factors by a server using deep learning. In this instance, the method for determining dementia risk factors includes acquiring biometric information from each subject corresponding to a first control group through a wearable device, acquiring measurement information for each subject corresponding to the first control group, deriving a first dementia risk factor based on the biometric information and the measurement information for each subject, and deriving a second dementia risk factor related to the first dementia risk factor via deep learning performed based on the biometric information related to the first dementia risk factor and control group information.

The present invention discloses a method for determining a maximum value of a heart activity parameter of a user performing a physical activity. Acquire first heart activity data in a first duration of the physical activity performed by the user. Acquire motion data in the first duration of the physical activity performed by the user. Calculate second heart activity data based on the motion data in the first duration of the physical activity performed by the user by a mathematical model and estimate the maximum value of the heart activity parameter of the user based on a comparison between the first heart activity data and the second heart activity data.

Electronic devices and methods are disclosed. First and second electronic devices include processors, which implement one or more methods, including receiving sensor data, determining an exercise starting timepoint based on first sensor data, estimating an exercise posture of a user based on second sensor data, estimating a pattern of change in distance between each of the plurality of external electronic devices, indicated by changes in the relative position between each the plurality of external electronic devices over a time period of the determined exercise starting timepoint, based on third sensor data, and generating exercise information based on the estimated exercise posture and the estimated pattern of change in distance between the plurality of external electronic devices.

Devices, systems, and techniques for controlling delivery of therapy for diabetes are described. In one example, a system includes a wearable device configured to generate user activity data associated with an arm of a user; and one or more processors configured to: identify at least one gesture indicative of utilization of an injection device for preparation of an insulin injection based on the user activity data; based on the at least one identified gesture, generate information indicative of at least one of an amount or type of insulin dosage in the insulin injection by the injection device; compare the generated information to a criteria of a proper insulin injection; and output information indicative of whether the criteria is satisfied based on the comparison.