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

CPS：TTP 选项：协同：协作研究：活动预测网络物理系统 (APCPS) 的科学

• 批准号: 1543656
• 负责人: Diane Cook
• 金额: $ 110万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1543656&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Synergy; Collaborative

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.

该项目旨在设计算法技术来根据传感器数据执行活动发现、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，包括系统响应性和适应性的潜在改进。这项工作的成果预计还将对医疗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行的健康干预措施。第二个应用程序将为建筑自动化提供具体的工具，以提高可持续性，而不会中断个人当前或即将进行的活动。项目研究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。每年两次的游览和博物馆展览将惠及 K-12 教师、学生和游客，持续的商业化工作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来进行活动学习、知识转移和人机交互计算，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类）传输到云中的活动模型、移动设备接口、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，围绕以下主旨进行组织：（1）设计用于活动发现、识别和预测的可扩展和通用的算法； （2）设计迁移学习方法，以提高活动预测网络物理系统的泛化能力； （3）设计人机交互计算方法，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估指标； (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。

项目成果

Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications

从传感器数据学习活动预测：算法、评估和应用

• DOI: 10.1109/tkde.2017.2750669
• 发表时间: 2017-12-01
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: B. Minor;J. Doppa;D. Cook
• 通讯作者: D. Cook

Real-time change point detection with application to smart home time series data

实时变化点检测及其应用于智能家居时间序列数据

• 发表时间: 2018-07
• 期刊: IEEE transactions on knowledge and data engineering
• 影响因子: 8.9
• 作者: Aminikhanghahi, Samaneh;Wang, Tinghui;Cook, Diane J
• 通讯作者: Cook, Diane J

Design of a smart home-driven digital memory notebook to support self-management of activities for older adults

设计智能家居驱动的数字记忆笔记本，支持老年人的活动自我管理

• 发表时间: 2018-07
• 期刊: Gerontechnology
• 作者: Dahmen, J;Minor, B;Cook, D J;Vo, T;Schmitter
• 通讯作者: Schmitter

Automatic assessment of functional health decline in older adults based on smart home data

基于智能家居数据自动评估老年人功能健康下降

• DOI: 10.1016/j.jbi.2018.03.009
• 发表时间: 2018-05
• 期刊: Journal of Biomedical Informatics
• 影响因子: 4.5
• 作者: Alberdi Aramendi A;Weakley A;Aztiria Goenaga A;Schmitter-Edgecombe M;Cook DJ
• 通讯作者: Cook DJ

Using smart offices to predict occupational stress

使用智能办公室预测职业压力

• 发表时间: 2018-09
• 期刊: International journal of industrial ergonomics
• 影响因子: 3.1
• 作者: Alberdi, A;Goenaga, A;Cook, D;Basarab, A
• 通讯作者: Basarab, A