Zero-effort ambient vitals monitoring

生命体征（例如，呼吸、血压、心率）提供关于人的健康的大量信息，并且它们的频繁测量对于诊断、监测和管理诸如充血性心力衰竭、肾衰竭和冠状动脉疾病的状况是基本的。使用传统设备（例如血压袖带）和可穿戴设备（例如智能手表）在家中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，这可以改善结果，提高独立性，并减少对诊所或医院的访问。然而，这些技术需要培训和勤奋使用才能有效，对于那些无法可靠使用这些技术的人来说，这不是一个可行的选择，例如痴呆症患者，关节炎和其他行动障碍患者以及负担过重的护理伙伴。这意味着，目前的远程监测方法排除了很大一部分可以显著受益的人群，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，而无需使用它们的人付出任何努力。 该计划的目的是研究零努力的生命体征监测方法。我们将开发传感器、数据处理和机器学习技术，以创建一个类似机器人的设备，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，一个关键的研究挑战是开发一个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。这将通过将现有的传感器与新型碳纳米管传感器融合来实现。我们将建立在信号处理方法，如自适应和非线性粒子滤波，以消除由运动引起的噪声和隔离感兴趣的信号。 这项研究还将调查使用热成像摄像机和高频雷达等传感器来捕捉一个人坐下前的活动。我们将应用机器学习来自动校准不同人的设备，并将生命体征与他们的活动水平联系起来。将由垫子捕获的生命体征（例如，心率升高）的变化与来自活动传感器的关于在进行测量之前人在做什么的数据相关联是一种新颖的方法，其将使得能够更全面地表示人的健康。 从这项研究中获得的知识和技术代表了传感器和机器学习的关键进步，这将使非临床环境中的数据收集与相关背景相结合，以便更全面地了解一个人的健康状况。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，包括为全球老龄化人口提供急需的支持。

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.