Department of Computer Science, University of California, Irvine, CA, USA.
School of Social Ecology, University of California, Irvine, CA, USA.
J Med Eng Technol. 2020 May;44(4):177-189. doi: 10.1080/03091902.2020.1759707. Epub 2020 Jun 26.
Monitoring people's stress levels has become an essential part of behavioural studies for physical and mental illnesses conducted within the biopsychosocial framework. There have been several stress assessment studies in laboratory-based controlled settings. However, the results of these studies do not always translate effectively to an everyday context. The current state of wearable sensor technology allows us to develop systems measuring the physiological signals reflecting stress 24/7 while capturing the context. In this paper, we present a stress monitoring system that provides objective daily stress measurements in everyday settings based on three physiological signals: electrocardiogram (ECG), photoplethysmogram (PPG), and galvanic skin response (GSR) using Shimmer3 ECG, Shimmer3 GSR+, and Empatica E4 wearable sensors. We perform controlled stress assessment experiments on 17 participants in which we successfully detect stress with a 94.55% accuracy for 10-fold cross-validation and an 85.71% accuracy for subject-wise cross-validation. In everyday settings, the system assesses stress with an 81.82% accuracy. We also examine whether motion artefacts affect stress assessment and filter the low-confidence readings to minimise false alarms.
监测人们的压力水平已经成为在生物心理社会框架内进行的身心疾病行为研究的重要组成部分。已经有几项在基于实验室的对照环境中进行的压力评估研究。然而，这些研究的结果并不总是能有效地转化为日常环境。当前的可穿戴传感器技术使我们能够开发出 24/7 测量反映压力的生理信号并同时捕捉环境的系统。在本文中，我们提出了一种压力监测系统，该系统基于三种生理信号（心电图 (ECG)、光体积描记图 (PPG) 和皮肤电反应 (GSR)），使用 Shimmer3 ECG、Shimmer3 GSR+和 Empatica E4 可穿戴传感器，在日常环境中提供客观的日常压力测量。我们在 17 名参与者中进行了受控的压力评估实验，其中我们成功地以 10 倍交叉验证的 94.55%准确率和以参与者为基础的交叉验证的 85.71%准确率检测到压力。在日常环境中，系统的压力评估准确率为 81.82%。我们还研究了运动伪影是否会影响压力评估，并过滤低置信度读数以最小化误报。
