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.

预计在未来20年中，加拿大和世界各地的老年人数量将增加一倍，以对我们的医疗保健系统和纳税人施加压力。结果，人们对设计**智能环境以支持现场老化的兴趣越来越大，同时包括确保老年人福祉**所需的监控。监视日常生活的活动（ADL）常规的家庭居住者揭示了有关其健康和福祉的重要信息。老年人倾向于有常规的ADL模式。与这些ADL例程的任何偏差**可能表明其健康状况需要进行干预的重要变化。因此，环境传感器是老年人的首选选择。在智能环境方面，环境感应能力的整合正在获得**作为一种可行的机制，以促进使用不可磨损设备的不引人注目的监测。使用环境中的环境传感器（例如运动传感器，相机和占用传感器）对ADL进行了**的监测。由于隐私问题，相机不是首选。其他环境传感器**需要传感器安装，并产生操作和维护成本。 WiFi是**环境传感器的有前途的替代品，对乘员活动极为敏感。它可以检测到高分辨率运动，并且**消除了其他传感器的成本。然而，尽管基于WiFi的活动识别可以建立在W​​iFi的**普遍存在和低成本的基础上，但它遇到了监视多个家庭居住者并确定**多次日常生活的挑战。该项目旨在通过提出数据分析来应对这些挑战**用于开发特定于主题的ADL模型的解决方案，这些模型利用了基线CSI **的变化，从固有的（例如，网络接口卡的配置）和外部因素（例如，环境配置）。利用这些新型模型，该系统将能够识别**异常活动并提供适当的警报。