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，以根据传感器数据进行活动发现，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，包括系统响应性和适应性的潜在改进。这项工作的成果预计也将对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个人日常环境中运作的健康干预措施。第二个应用程序将为楼宇自动化提供具体的工具，提高可持续性，而不会中断个人当前或即将进行的活动。项目调查人员将利用现有的培训计划，让来自代表性不足群体的学生参与这项研究。每年两次的图尔斯之旅和博物馆展览将覆盖K-12教师、学生和游客，持续的商业化努力将确保设计的技术可供公众使用。“在野外”部署活动预测网络物理系统需要许多强大的计算组件，用于活动学习、知识转移、和人在回路中的计算，都是这个项目的一部分。然后，这些组件创建网络物理系统，该网络物理系统将来自感测环境（物理设置以及环境中的人）的信息汇集到云中的活动模型、移动终端接口、智能电网，然后返回到环境。拟议的研究中心是定义活动预测网络物理系统的科学，围绕以下主题组织：（1）设计用于活动发现，识别和预测的可扩展和可推广的算法;（2）设计迁移学习方法，以提高概括活动预测网络物理系统的能力;（3）设计人在回路计算方法，以增加活动预测信息物理系统的灵敏度;（4）引入活动预测信息物理系统的评估指标;以及（5）将活动预测网络物理系统过渡到实际应用，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.