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: 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.