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状况的丰富信息，频繁测量生命体征是诊断、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。使用传统设备（如血压袖带）和可穿戴设备（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，从而改善结果，提高独立性，并减少去诊所或医院的次数。然而，这些技术需要经过培训和勤奋使用才能发挥作用，对于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、关节炎患者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能显著受益的人口，包括许多老年人。我们需要能够在家庭环境中可靠地采集生命体征的新技术，而无需使用者付出任何努力。******该计划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，创造一种类似垫子的设备，可以放在任何椅子或沙发上，当一个人坐在上面时，它可以自动捕捉生命体征。由于传统的监测需要皮肤接触，一个关键的研究挑战是获得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传感器融合来实现。我们将建立信号处理方法，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。******这项研究还将研究使用热像仪和高频雷达等传感器来捕捉一个人坐下之前的活动。我们将应用机器学习来自主校准设备，以适应不同的人，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变化（例如心率升高）与活动传感器提供的有关在进行测量之前人们正在做什么的数据相关联，是一种新颖的方法，可以更全面地反映一个人的健康状况。******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，这将使在非临床环境中收集相关背景的数据成为可能，从而更全面地了解一个人的健康状况。这项研究将提升加拿大在环境监测技术方面的领先地位，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.