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

加拿大和世界各地的老年人数量预计将在未来20年内翻一番 ** 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就地养老，同时包括确保老年人福祉所需的监控。监测家庭居住者的日常生活活动（ADL）例程揭示了有关其健康和福祉的重要信息。老年人往往有常规的ADL模式。任何偏离 ** 这些日常生活活动的常规可能表明他们的健康状况发生了重要变化，需要进行干预。虽然可穿戴传感器是一种替代方案，但老年人往往会忘记随身携带（例如 **在晚上去洗手间时，或在洗澡时）。因此，环境传感器是老年人的首选。关于智能环境，环境传感功能的集成作为一种可行的机制正在获得越来越多的支持，以促进使用非可穿戴设备进行不引人注目的监控。ADL已经 ** 使用部署在环境中的环境传感器（如运动传感器、摄像机和占用传感器）进行监测。出于隐私考虑，相机不是首选。其他环境传感器 ** 需要安装传感器并产生操作和维护成本。WiFi是 ** 环境传感器的一个有前途的替代品，对居住者的活动非常敏感。它可以检测高分辨率的运动，** 避免额外的传感器的成本。然而，虽然基于WiFi的活动识别可以建立在WiFi的普及性和低成本的基础上，但它在监视多个家庭居住者和识别日常生活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 ** 解决方案来应对这些挑战，以开发特定于受试者的ADL模型，这些模型利用了基线CSI** 测量的变化，网络接口卡的配置）和外在因素（例如，**环境的配置）。利用这些新模型，该系统将能够识别异常活动并提供适当的警报。

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.