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

NSF 10, # 8208;575 SHB：大型：合作研究：家庭中老年人持续协调护理的集成通信和推理系统[i] Bajcsy， UC Berkeley, Co&#8208；PI Jimison， ohsu【摘要】这个研究项目解决了改善和维持人类的重要问题。通过基于基本科学原理的智能技术发明健康的生活方式。这些办法在经济上是可行的，在社会上是有吸引力的，重点是在家庭环境中保持老年人的健康和独立。该项目混合使用网络和监控技术，将老年人与远程健康教练（由半自动化程序提供便利的真人）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。该干预措施旨在提供协调和持续的健康管理。该项目的研究使用了来自家庭中各种传感器的数据集成，为活动监测、睡眠监测、步态和运动分析、社会化措施以及来自计算机与适应性游戏交互的各种认知指标提供信息。使用患者认知和身体功能的严格计算工程模型，以及上下文和环境，来推断患者的状态，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察马尔可夫过程和混合控制模式。然后使用包含行为改变原则的用户模型来驱动算法，以优化自动反馈和建议，为管理大量患者的健康教练提供提示。通过利用现有的原型平台来评估这些远程健康管理方法，该平台具有从老年参与者家中收集数据的能力。

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.