This research project addresses the important problem of improving and maintaining peoples' healthy lifestyles by inventing smart technology based on fundamental scientific principles. The approaches are economically feasible and socially compelling approaches, with a focus on maintaining the health and independence of older adults in a home environment. The project uses a mix of networking and monitoring technologies to connect older adults with a remote health coach (real person facilitated by a semi-automated program) and remote family members. One of the key design issues is how best to preserve privacy and enable the participants to control the distribution and sharing of their data. The intervention is designed to provide coordinated and continuous health management.

The research for this project uses the integration of data from a variety of sensors in the home, yielding information for activity monitoring, sleep monitoring, gait and movement analysis, socialization measures, as well as a variety of cognitive metrics derived from computer interactions with adaptive games. Rigorous computational engineering models of the cognitive and physical functions of the patient, as well as context and environment, are used to infer patient state and provide feedback for the patient and the remote health coach. The modeling techniques include Partially Observable Markov Process and Hybrid Control Modes. User models that incorporate behavior change principles are then used to drive algorithms to optimize automated feedback and recommendations that serve as prompts for a health coach managing a large number of patients. These approaches to remote health management are evaluated by leveraging an existing prototype platform with the capability of collecting data from the homes of elderly participants.

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University of California Berkeley
United States
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