CPS: TTP Option: Synergy: Collaborative Research: The Science of Activity-Predictive Cyber-Physical Systems (APCPS)

该项目旨在设计算法技术来从传感器数据中执行活动发现、识别和预测。这些技术将构成活动预测网络物理系统科学的基础，包括对系统响应和适应能力的潜在改进。这项工作的结果预计还将对卫生保健和可持续性这两个具有社会重要性的优先领域的具体应用领域产生重要影响。第一个应用程序将允许提供适应个人日常生活并在该人的日常环境中运作的卫生干预措施。第二个应用程序将提供建筑自动化的具体工具，在不中断个人当前或即将进行的活动的情况下提高可持续性。项目调查人员将利用现有的培训计划，让代表人数不足的群体的学生参与这项研究。每两年一次的参观和博物馆展览将接触到K-12的教师、学生和参观者，正在进行的商业化努力将确保设计的技术可供公众使用。部署活动预测的网络物理系统需要一些强大的计算组件，用于活动学习、知识传递和人在环中计算，这些组件是作为该项目的一部分引入的。然后，这些组件创建网络物理系统，将信息从感知环境(物理环境以及环境中的人)、云中的活动模型、移动设备接口、智能电网，然后返回环境。这项研究围绕以下几个方面展开：(1)设计可扩展和可推广的活动发现、识别和预测算法；(2)设计迁移学习方法以提高推广活动预测网络物理系统的能力；(3)设计人在环计算方法以提高活动预测网络物理系统的灵敏度；(4)引入活动预测网络物理系统的评价指标；以及(5)活动预测网络物理系统向实际应用的过渡，包括健康监测/干预和智能/可持续城市。

This project aims to design algorithmic techniques to perform activity discovery, recognition, and prediction from sensor data. These techniques will form the foundation for the science of Activity- Prediction Cyber-Physical Systems, including potential improvement in the responsiveness and adaptiveness of the systems. The outcome of this work is also anticipated to have important implications in the specific application areas of health care and sustainability, two priority areas of societal importance. The first application will allow for health interventions to be provided that adapt to an individual's daily routine and operate in that person's everyday environment. The second application will offer concrete tools for building automation that improve sustainability without disrupting an individual's current or upcoming activities. The project investigators will leverage existing training programs to involve students from underrepresented groups in this research. Bi-annual tours and a museum exhibit will reach K-12 teachers, students and visitors, and ongoing commercialization efforts will ensure that the designed technologies are made available for the public to use.Deploying activity-predictive cyber-physical systems "in the wild" requires a number of robust computational components for activity learning, knowledge transfer, and human-in- the-loop computing that are introduced as part of this project. These components then create cyber physical systems that funnel information from a sensed environment (the physical setting as well as humans in the environment), to activity models in the cloud, to mobile device interfaces, to the smart grid, and then back to the environment. The proposed research centers on defining the science of activity-predictive cyber-physical systems, organized around the following thrusts: (1) the design of scalable and generalizable algorithms for activity discovery, recognition, and prediction; (2) the design of transfer learning methods to increase the the ability to generalize activity-predictive cyber-physical systems; (3) the design of human-in-the-loop computing methods to increase the sensitivity of activity-predictive cyber-physical systems; (4) the introduction of evaluation metrics for activity-predictive cyber-physical systems; and (5) transition of activity-predictive cyber-physical systems to practical applications including health monitoring/intervention and smart/sustainable cities.