IEEE J Biomed Health Inform. 2023 May;27(5):2155-2165. doi: 10.1109/JBHI.2023.3239305. Epub 2023 May 4.
Stress is an inevitable part of modern life. While stress can negatively impact a person's life and health, positive and under-controlled stress can also enable people to generate creative solutions to problems encountered in their daily lives. Although it is hard to eliminate stress, we can learn to monitor and control its physical and psychological effects. It is essential to provide feasible and immediate solutions for more mental health counselling and support programs to help people relieve stress and improve their mental health. Popular wearable devices, such as smartwatches with several sensing capabilities, including physiological signal monitoring, can alleviate the problem. This work investigates the feasibility of using wrist-based electrodermal activity (EDA) signals collected from wearable devices to predict people's stress status and identify possible factors impacting stress classification accuracy. We use data collected from wrist-worn devices to examine the binary classification discriminating stress from non-stress. For efficient classification, five machine learning-based classifiers were examined. We explore the classification performance on four available EDA databases under different feature selections. According to the results, Support Vector Machine (SVM) outperforms the other machine learning approaches with an accuracy of 92.9 for stress prediction. Additionally, when the subject classification included gender information, the performance analysis showed significant differences between males and females. We further examine a multimodal approach for stress classifications. The results indicate that wearable devices with EDA sensors have a great potential to provide helpful insight for improved mental health monitoring.
压力是现代生活不可避免的一部分。虽然压力会对人的生活和健康产生负面影响，但积极且适度的压力也可以促使人们针对日常生活中遇到的问题产生创造性的解决方案。虽然很难消除压力，但我们可以学习监测和控制其生理和心理影响。为了提供更多的心理健康咨询和支持计划，以帮助人们缓解压力和改善心理健康，提供可行和即时的解决方案至关重要。一些流行的可穿戴设备，如具有多种传感功能的智能手表，包括生理信号监测，可以缓解这个问题。这项工作研究了使用可穿戴设备上采集的基于手腕的皮肤电活动（EDA）信号来预测人们的压力状态并识别可能影响压力分类准确性的因素的可行性。我们使用从手腕佩戴设备上采集的数据来研究从无压力状态到压力状态的二元分类。为了实现有效的分类，我们检查了五种基于机器学习的分类器。我们在不同的特征选择下，探索了四个可用的 EDA 数据库上的分类性能。根据结果，支持向量机（SVM）的分类性能优于其他机器学习方法，其对压力的预测准确率达到 92.9%。此外，当主体分类包含性别信息时，性能分析显示男性和女性之间存在显著差异。我们进一步检查了一种用于压力分类的多模态方法。结果表明，具有 EDA 传感器的可穿戴设备在改善心理健康监测方面具有很大的潜力。
