CRII: CHS: WiFi-Based Human Behavior Sensing and Recognition System for Aging in Place

CRII：CHS：基于 WiFi 的人类行为感知和识别系统，用于就地养老

• 批准号: 1565604
• 负责人: Mi Zhang
• 金额: $ 17.16万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565604&HistoricalAwards=false
• 关键词: CRII; CHS; WiFi; Based; Human

As "baby boomers" age, the United States will experience considerable growth in its elderly population over the coming years. Studies consistently confirm that the majority of older adults would prefer to remain in their own homes for as long as possible. Therefore, there is a critical need for home-based assisted living technologies capable of continuously yet unobtrusively monitoring activities of daily living (ADLs) and detecting abnormal events, both to reduce the cost of elder care and to enhance the quality of life. Current human behavior monitoring systems for aging in place, which are typically based on cameras, smartphone/wearable devices, or ambient sensors, have fundamental limitations such as high cost and invasion of privacy that prevent them from being widely deployed. The PI's objective in this project is to build on his prior work to establish a research program to investigate a new approach to aging in place that harnesses the now-ubiquitous commercial home WiFi signals to monitor ADLs and detect abnormal events. The central idea is that different human activities cause different changes in WiFi signals; by analyzing these changes, the activity that caused the change can be recognized. This work will have broad societal impact both within the United States and abroad, by contributing to new techniques and systems for WiFi-based human behavior sensing and recognition in both single-subject and multi-subject scenarios. If the new system is effective, it will provide a non-intrusive, device-free, low-cost and privacy-preserving assisted living technology for aging in place. The PI will integrate research results from this project into both his undergraduate and graduate courses, as well as the K-12 education program; furthermore, the hardware and software developed in this research will be open-source, and the dataset collected during this project will be made available to others for further research.The PI plans to exploit the fine-grained PHY layer Channel State Information (CSI) extracted from the WiFi signals as the basis for a unified scheme for monitoring both the most common stationary and moving activities performed daily by older adults in their homes. He will detect stationary activities by tracking the minute but periodic chest movements caused by breathing, and he will extract frequency domain features to robustly recognize the same moving activity even with different movement directions or at different locations. The PI will develop Markov models to recognize complex ADLs, and he will leverage the breathing and physical body movement information to detect abnormal behaviors including accidental falls and disturbed sleep that are potential issues relating to aging in place. Ultimately, the PI will extend his techniques to recognize ADLs of multiple persons performed at the same time. To successfully achieve these objectives, the PI will need to overcome a number of significant technical challenges, for example detecting minute changes in the WiFi signal due to stationary activities such as working at a computer or watching TV while seated on a sofa. Robustly recognizing the same moving activity (e.g., housecleaning) performed in different ways or at different locations will also be tricky, because different movement directions or different layouts at different locations cause different disturbances to WiFi signals.

随着“婴儿潮一代”的老龄化，美国老年人口在未来几年将大幅增长。 研究一致证实，大多数老年人更愿意尽可能长时间地呆在自己的家中。 因此，迫切需要能够持续且不引人注目地监测日常生活活动（ADL）并检测异常事件的家庭辅助生活技术，以降低老年人护理成本并提高生活质量。 当前的就地老龄化人类行为监测系统通常基于摄像头、智能手机/可穿戴设备或环境传感器，但存在成本高和侵犯隐私等根本局限性，阻碍了其广泛部署。 PI 在该项目中的目标是在他之前的工作基础上建立一个研究计划，以研究一种新的就地养老方法，该方法利用现在无处不在的商业家庭 WiFi 信号来监控 ADL 并检测异常事件。 中心思想是不同的人类活动导致WiFi信号的不同变化；通过分析这些变化，可以识别导致变化的活动。 这项工作将为单主体和多主体场景中基于 WiFi 的人类行为感知和识别的新技术和系统做出贡献，从而在美国和国外产生广泛的社会影响。 如果新系统有效，它​​将为居家养老提供一种非侵入式、无设备、低成本且保护隐私的辅助生活技术。 PI将把该项目的研究成果整合到他的本科生和研究生课程以及K-12教育计划中；此外，本研究中开发的硬件和软件将开源，并且该项目期间收集的数据集将可供其他人进行进一步研究。PI 计划利用细粒度 PHY 层通道状态信息 (CSI)从 WiFi 信号中提取，作为统一方案的基础，用于监控老年人在家中每天进行的最常见的固定和移动活动。 他将通过跟踪由呼吸引起的微小但周期性的胸部运动来检测静止活动，并且他将提取频域特征以稳健地识别相同的运动活动，即使运动方向不同或在不同位置也是如此。 PI 将开发马尔可夫模型来识别复杂的 ADL，他将利用呼吸和身体运动信息来检测异常行为，包括意外跌倒和睡眠不安，这些都是与原位老化相关的潜在问题。 最终，PI 将扩展他的技术来识别多人同时执行的 ADL。 为了成功实现这些目标，PI 需要克服许多重大技术挑战，例如检测由于固定活动（例如在电脑前工作或坐在沙发上看电视）导致的 WiFi 信号的微小变化。 鲁棒地识别以不同方式或在不同位置执行的相同移动活动（例如，打扫房间）也很棘手，因为不同的移动方向或不同位置的不同布局会对 WiFi 信号造成不同的干扰。