Stress Detection through Wrist-based Electrodermal Activity Monitoring and Machine Learning

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 5; pp. 1 - 11
• Main Authors: Zhu, Lili, Spachos, Petros, Ng, Pai Chet, Yu, Yuanhao, Wang, Yang, Plataniotis, Konstantinos, Hatzinakos, Dimitrios
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anxiety disorders; Biomedical monitoring; Classification; EDA; Electronic devices; emotion recognition; Feasibility; Female; Galvanic Skin Response; Human factors; Humans; k-nearest neighbors; Learning algorithms; logistic regression; Machine Learning; Male; Mental health; Monitoring; Monitoring, Ambulatory - methods; naive bayes; Psychological effects; random forest; Sensors; Signal monitoring; Skin; smartwatches; Stress; Stress measurement; Support vector machines; Wearable computers; Wearable Electronic Devices; wearable sensors; Wearable technology; Wrist; Wrist - physiology; wrist-worn wearable device
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2023.3239305

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.