SHB: Large: Collaborative Research: Integrated Communications and Inference Systems for Continuous Coordinated Care of Older Adults in the Home

NSF 10& #8208; 575 SHB：大号：合作研究：综合通信和推理系统为持续协调照顾老年人在家里PI Bajcsy，加州大学伯克利分校，公司‐PI Jimison，OHSU摘要本研究项目解决的重要问题，改善和维护人民？通过发明基于基本科学原理的智能技术来实现健康的生活方式。这些方法在经济上是可行的，在社会上是有说服力的，重点是在家庭环境中保持老年人的健康和独立。 该项目使用网络和监控技术的组合，将老年人与远程健康教练（由半自动程序提供便利的真实的人）和远程家庭成员联系起来。 关键的设计问题之一是如何最好地保护隐私，并使参与者能够控制其数据的分发和共享。该干预旨在提供协调和持续的健康管理。该项目的研究使用来自家庭中各种传感器的数据集成，产生用于活动监测，睡眠监测，步态和运动分析，社会化措施的信息，以及来自计算机与自适应游戏交互的各种认知指标。 患者的认知和身体功能以及上下文和环境的严格计算工程模型用于推断患者状态并为患者和远程健康教练提供反馈。 建模技术包括部分可观测马尔可夫过程和混合控制模式。 然后，结合行为改变原则的用户模型被用于驱动算法，以优化自动反馈和建议，这些反馈和建议可以作为管理大量患者的健康教练的提示。 这些远程健康管理的方法进行了评估，利用现有的原型平台，从家中收集数据的老年参与者的能力。

NSF 10‐575 SHB: Large: Collaborative Research: Integrated Communication and Inference Systems forContinuous Coordinated Care of Older Adults in the HomePI Bajcsy, UC Berkeley, Co‐PI Jimison, OHSUAbstractThis 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.