IEEE J Biomed Health Inform. 2019 Sep;23(5):1920-1927. doi: 10.1109/JBHI.2018.2877484. Epub 2018 Oct 29.
This paper studies the feasibility of using low-cost motion sensors to provide opportunistic heart rate assessments from ballistocardiographic signals during restful periods of daily life. Three wearable devices were used to capture peripheral motions at specific body locations (head, wrist, and trouser pocket) of 15 participants during five regular workdays each. Three methods were implemented to extract heart rate from motion data and their performance was compared to those obtained with an FDA-cleared device. With a total of 1358 h of naturalistic sensor data, our results show that providing accurate heart rate estimations from peripheral motion signals is possible during relatively "still" moments. In our real-life workplace study, the head-mounted device yielded the most frequent assessments (22.98% of the time under 5 beats per minute of error) followed by the smartphone in the pocket (5.02%) and the wrist-worn device (3.48%). Most importantly, accurate assessments were automatically detected by using a custom threshold based on the device jerk. Due to the pervasiveness and low cost of wearable motion sensors, this paper demonstrates the feasibility of providing opportunistic large-scale low-cost samples of resting heart rate.
本研究旨在探讨利用低成本运动传感器从日常生活中的心动冲击图信号中获取机会性心率评估的可行性。在五个工作日期间，15 名参与者佩戴三个可穿戴设备，在特定身体部位（头部、手腕和裤兜）采集外周运动数据。本研究共采集了 1358 小时的自然状态下的传感器数据，实施了三种从运动数据中提取心率的方法，并将其性能与经 FDA 认证的设备进行了比较。结果表明，在相对“静止”的时刻，从外周运动信号中提供准确的心率估计是可行的。在我们的真实工作场所研究中，头戴式设备的评估最频繁（22.98%的时间误差在 5 次/分钟以下），其次是口袋里的智能手机（5.02%）和手腕佩戴设备（3.48%）。最重要的是，通过使用基于设备急动度的自定义阈值，可以自动检测到准确的评估。由于可穿戴运动传感器的普及性和低成本，本研究证明了提供机会性、大规模、低成本的静息心率样本的可行性。
