Non-instrusive health monitoring

近十年来，远程健康和健康监测一直是公共医疗领域的热门话题，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达到预期。健康可穿戴设备的兴起带来了一系列隐私挑战。虽然患者可能愿意忍受随时佩戴这些技术带来的不便，但普通用户认为它们是侵入性的。在跌倒检测通知的情况下，当前的技术水平要求 ** 人在福尔斯跌倒之后主动触发设备以呼叫帮助，或者在 ** 基于移动的设备的情况下使用位置感测来自动发送信号以寻求帮助。视频监控内容丰富，但可能会有干扰。无论准确性和侵入性水平如何，所有现有的跌倒检测方法都限于检测跌倒和发出求救信号，从而使它们过于简单，并且经常不足以用于跌倒检测中的实际应用，更重要的是，跌倒后的安全和跌倒预防。我们提出的研究旨在开发一个平台，将独特的非侵入式视频跌倒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预测跌倒并报警求助，识别跌倒的可能原因，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，以支持该个人的家庭健康。这种纵向跟踪也将有助于确定随着时间的推移，个人如何变化，以及智能环境应如何适应这种变化。拟议的监测支持当前加拿大和不列颠哥伦比亚省对家庭健康的重视，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，以改善医疗保健和健康结果。

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.