Smart environment monitoring through data analytics processing of WiFi to detect activities of daily living

通过 WiFi 数据分析处理来检测日常生活活动，实现智能环境监控

• 批准号: 530965-2018
• 负责人: Cretu, AnaMaria
• 金额: $ 1.82万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=648292
• 关键词: Smart; environment; monitoring; through; data

The number of older adults in Canada and around the world is expected to double over the next 20 years**exerting pressure on our healthcare systems and taxpayers. As a result, there is growing interest in designing**smart environments to support aging-in-place, while including the monitoring required to ensure the well-being**of older adults. Monitoring the Activities of Daily Living (ADL) routines of home occupants reveals important**information about their health and well-being. Older adults tend to have routine ADL patterns. Any deviations**from these ADL routines may indicate important changes in their health status necessitating an intervention.**Although wearable sensors are an alternative, older adults can often forget to carry them on their person (e.g.**while going to the washroom during the night, or while bathing). Ambient sensors are thus a preferred option**for older adults. With respect to smart environments, the integration of ambient sensing capabilities is gaining**ground as a viable mechanism to facilitate unobtrusive monitoring with non-wearable devices. ADLs have**been monitored using ambient sensors (such as motion sensors, cameras, and occupancy sensors) that are**deployed in the environment. Cameras are not preferred because of privacy concerns. Other ambient sensors**require sensor installation and incur operational and maintenance costs. WiFi is a promising alternative to**ambient sensors and is extremely sensitive to occupant activity. It can detect high-resolution movements and**obviates cost of additional sensors. Yet, while WiFi-based activity recognition can build on both WiFi's**pervasiveness and low cost, it encounters challenges for monitoring multiple home occupants and identifying**multiple activities of daily living. This project aims at tackling these challenges by proposing data analytics**solutions for the development of subject-specific ADL models that capitalize on changes to the baseline CSI**measurements from both intrinsic (e.g., configuration of the network interface card) and extrinsic factors (e.g.,**configuration of the environment). Making use of these novel models, the system will be capable of identifying**abnormal activity and provide appropriate alerts.

加拿大和世界各地的老年人数量预计将在未来 20 年翻一番**，给我们的医疗保健系统和纳税人带来压力。因此，人们越来越有兴趣设计**智能环境来支持居家养老，同时包括确保老年人福祉**所需的监控。监测住户的日常生活活动 (ADL) 可以揭示有关他们健康和福祉的重要**信息。老年人往往有常规的日常生活活动模式。任何偏离**这些日常生活习惯的行为都可能表明他们的健康状况发生了重大变化，需要进行干预。**尽管可穿戴传感器是一种替代方案，但老年人经常会忘记将它们随身携带（例如**在下班期间去洗手间时）晚上或洗澡时）。因此，环境传感器是老年人的首选**。在智能环境方面，环境传感功能的集成作为一种可行的机制正在取得进展，以促进非可穿戴设备的不引人注目的监控。 ADL 使用部署在环境中的环境传感器（例如运动传感器、摄像头和占用传感器）进行监控。由于隐私问题，相机不是首选。其他环境传感器**需要安装传感器并产生运营和维护成本。 WiFi 是**环境传感器的一种很有前途的替代品，并且对居住者的活动极其敏感。它可以检测高分辨率运动并**消除额外传感器的成本。然而，虽然基于 WiFi 的活动识别可以建立在 WiFi 的普及性和低成本之上，但它在监控多个家庭住户和识别日常生活中的多种活动方面遇到了挑战。该项目旨在通过提出数据分析**解决方案来开发特定主题的 ADL 模型来应对这些挑战，该模型利用内在（例如，网络接口卡的配置）和外在因素对基线 CSI** 测量的变化（例如，**环境配置）。利用这些新颖的模型，系统将能够识别**异常活动并提供适当的警报。