Zero-effort ambient vitals monitoring

生命体征(如呼吸、血压、心率)提供了关于一个人健康的丰富信息，其频繁测量是诊断、监测和管理充血性心力衰竭、肾衰竭和冠状动脉疾病等疾病的基础。在家中使用传统设备(如血压计)和可穿戴设备(如智能手表)进行远程监测，可以提供对疾病管理和治疗至关重要的信息，从而改善结果、提高独立性并减少到诊所或医院就诊。然而，这些技术需要培训和勤奋使用才能有效，对于不能可靠地使用这些技术的人，如痴呆症患者、关节炎患者和其他行动不便的人，以及负担过重的护理伙伴来说，这不是一个可行的选择。这意味着，目前的远程监测方法将很大一部分受益人口排除在外，包括许多老年人。我们需要新的技术，可以在家庭环境中可靠地采样生命体征，而不需要任何使用它们的人。*这个项目的目的是研究零努力生命体征监测方法。我们将开发传感器、数据处理和机器学习技术，以创造一种类似垫子的设备，可以放置在任何椅子或沙发上，当一个人坐在上面时，自动捕捉生命体征。由于传统的监测需要皮肤接触，因此一个关键的研究挑战是获得一种传感器和信号处理方法的组合，可以通过衣服获取生命体征。这将通过将现有传感器与新型碳纳米管融合来实现。我们将建立在自适应和非线性粒子滤波等信号处理方法的基础上，以消除运动引起的噪声并分离感兴趣的信号。*这项研究还将研究使用热像仪和高频雷达等传感器来捕捉人在坐下前的活动。我们将应用机器学习来自主地为不同的人校准设备，并将生命体征与他们的活动水平联系起来。将坐垫捕捉到的生命体征(例如心率加快)的变化与活动传感器的数据相关联，这是一种新的方法，可以更全面地表示人的健康状况。*这项研究产生的知识和技术代表了传感器和机器学习方面的关键进步，这将使非临床环境中的数据收集与相关背景相关联，从而更全面地了解人的健康。这项研究将提升加拿大作为环境监测技术领导者的地位，并将使许多应用领域受益，包括为我们的全球老龄化人口提供亟需的支持。

Vital signs (e.g. respiration, blood pressure, heart rate) provide a wealth of information about a person's health and their frequent measurement is fundamental to diagnosing, monitoring, and managing conditions such as congestive heart failure, kidney failure, and coronary artery disease. Remote monitoring at home with traditional devices (e.g. blood pressure cuff) and wearable devices (e.g. smart watch) can provide information crucial to disease management and treatment, which can improve outcomes, increase independence, and reduce visits to a clinic or hospital. However, these technologies require training and diligent use to be effective, which is not a viable option for people who are not able to use these technologies reliably, such as people with dementia, people with arthritis and other mobility impairments, and overburdened care partners. This means that current remote monitoring methods are excluding a large portion of the population who stand to benefit significantly, including many older adults. We need new technologies that can reliably sample vitals in a home environment without any effort from the people using them.******The purpose of this program is to investigate zero-effort vital signs monitoring approaches. We will develop sensors, data processing, and machine learning techniques to create a cushion-like device that can be placed on any chair or sofa to autonomously capture vitals signs while a person is sitting on it. As conventional monitoring requires skin contact, a key research challenge is to derive a combination of sensors and signal processing methods that can acquire vitals through clothing. This will be accomplished by fusing existing sensors with novel carbon nanotube ones. We will build on signal processing methods such as adaptive and non-linear particle filtering to remove noise caused by movement and isolate signals of interest.******This research will also investigate the use of sensors such as thermal cameras and high-frequency radar to capture a person's activity prior to sitting down. We will apply machine learning to autonomously calibrate the device to different people and connect vitals to their level of activity. Correlating changes in vital signs (e.g. elevated heart rate) captured by the cushion to data from the activity sensors about what a person was doing before the measurement was taken is a novel approach that will enable a more comprehensive representation of a person's health.******The knowledge and technology resulting from this research represents pivotal advancements in sensors and machine learning that will enable data collection in non-clinical settings with associated context for a more holistic understanding of a person's health. This research will advance Canada's position as a leader in ambient monitoring technology and will benefit many application areas, including providing much-needed support for our globally aging population.