Non-instrusive health monitoring

近一年来，远程健康和健康监测一直是公共卫生保健领域感兴趣的话题 十年来，先进的诊断解决方案有望塑造我们的生活方式，但到目前为止还没有 期望值。健康可穿戴设备的兴起带来了一系列隐私挑战。而患者可能会 愿意承受随时随地佩戴这些技术带来的不便，平均而言 用户认为它们是侵入性的。在跌倒检测通知的情况下，当前最先进的技术要求 跌倒后主动触发设备呼救的人员，或在发生以下情况时使用位置感应 基于移动电话的设备，可自动发送求救信号。视频监控内容丰富，但可能 太无礼了。无论准确性和侵入性程度如何，所有现有的跌倒检测方法都限于 检测坠落并发出求救信号，从而使它们变得简单，并且经常不适合在跌倒检测以及更重要的跌倒后安全和跌倒预防中的实际应用。我们提出的研究旨在开发一个平台，将独特的非侵入性视频坠落检测方案与助手通知联系起来。我们的系统不仅可以在跌倒发生之前进行预测和报警，识别可能的跌倒原因，而且还可以将视频信号与其他传感器信号相关联，以提高预测的准确性。我们的项目还将解决明智的考虑，即在个人的健康记录中捕获和保留哪些相关数据，以支持个人的家庭健康。这种纵向跟踪还将有助于确定随着时间的推移，个体如何变化，以及智能环境应该如何适应这种变化。 拟议的监测支持加拿大和不列颠哥伦比亚省目前对家庭健康的重视，并提供了一个理想的加拿大机会，以评估其与传统传感协同作用的可行性，以改善卫生保健和健康结果。

Remote health and wellness monitoring has been a topic of interest in the field of public healthcare for almost a decade, with advanced diagnostic solutions promising to shape our lifestyle, but so far falling short of expectations. The rise of health wearable devices has brought a host of privacy challenges. While patients may be willing to subject themselves to the inconvenience of wearing these technologies at all times, the average users see them as invasive. In the case of fall detection notification, current state-of-the-art requires either the persons actively triggering the devices to call for help after falls, or using positional sensing in the case of mobile phone based devices to automate signaling for help. Video monitoring is rich in content but may be intrusive. Regardless of accuracy and level of intrusiveness, all existing fall detection methods are limited to detecting the fall and signalling for help, thereby rendering them simplistic and frequently inadequate for practical applications in fall detection, and more importantly, post-fall safety and fall prevention. Our proposed research seeks to develop a platform that links a unique non-intrusive video fall detection scheme with helper notification. Our system will not only work towards predicting fall before it happens and alert for help, identifying the possible origin of fall, but also associate the video signals with other sensor signals to improve accuracy of prediction. Our project will also address the judicious consideration of what relevant data to capture and retain in the person's health record to support wellness-in-home for that individual. This longitudinal tracking will also help to ascertain over time how the individual changes and how the smart environment should adapt to this change. The proposed monitoring supports current Canada and BC emphasis on home wellness and presents an ideal Canada-based opportunity to assess its feasibility in synergy with traditional sensing to improve health care and health outcomes.