Abstract: Various embodiments are directed to video-based deep learning evaluation of cardiac ultrasound that accurately identify cardiomyopathy and predict ejection fraction, the most common metric of cardiac function. Embodiments include systems and methods for analyzing images obtained from an echocardiogram. Certain embodiments include receiving video from a cardiac ultrasound of a patient illustrating at least one view the patient's heart, segmenting a left ventricle in the video, and estimating ejection fraction of the heart. Certain embodiments include at least one machine learning algorithm.

Abstract: A method for monitoring health of a person by a health monitoring system includes: measuring movement data of a person using a wearable health monitoring device, determined whether the person is in a deep sleep mode based on the movement data, automatically measuring a respiratory rate of the person that is in a deep sleep mode, determining that the person is in one of a plurality of respiration zones, measuring at least one of a body temperature, an ambient temperature, a sleeping position, a smoke or carbon monoxide level, a video, and images of the person, and automatically producing an alarm signal if a predetermined criterion is met based on the respiration zone and at least one of the body temperature, the ambient temperature, the smoke level, the carbon monoxide level, a sleeping position, or the images of the person.

Abstract: A method for monitoring health of a person by a health monitoring system includes determining if a person wearing a wearable health monitoring device is sleeping or not based on movement data, measuring sleep movements of the person to determine whether the person is in a deep sleep mode when the person is determined to be sleeping, measuring bio-vital signals of the person and at least one of a posture of the person or environmental parameters when the person is determined to be in the deep sleep mode, and automatically producing an alarm signal if a predetermined criterion is met based on the bio-vital signals and at least one of a posture of the person or the environmental parameters.

Abstract: Various embodiments are directed to video-based deep learning evaluation of cardiac ultrasound that accurately identify cardiomyopathy and predict ejection fraction, the most common metric of cardiac function. Embodiments include systems and methods for analyzing images obtained from an echocardiogram. Certain embodiments include receiving video from a cardiac ultrasound of a patient illustrating at least one view the patient's heart, segmenting a left ventricle in the video, and estimating ejection fraction of the heart. Certain embodiments include at least one machine learning algorithm.

Abstract: A computer-implemented method for recognizing a user's activity pattern includes pre-storing activity data in a computer system, automatically determining locations of one or more sensors on a user's body, obtaining time series of measured activity parameters by the one or more sensors, automatically segmenting the time series of measured activity parameters into two or more activity periods, determining a spatial range of the movement in an activity period, and recognizing an activity in the activity period based at least in part on the measured activity parameters and the pre-stored activity data.

Abstract: A computer-implemented method for recognizing a user's activity pattern includes pre-storing activity data in a computer system, automatically determining locations of one or more sensors on a user's body, obtaining time series of measured activity parameters by the one or more sensors, automatically segmenting the time series of measured activity parameters into two or more activity periods, determining a spatial range of the movement in an activity period, and recognizing an activity in the activity period based at least in part on the measured activity parameters and the pre-stored activity data.