Machine learning-based classification analysis of knowledge worker mental stress - Kim, Hyunsuk; Kim, Minjung; Park, Kyounghyun; Kim, Jungsook; Yoon, Daesub; Kim, Woojin; Park, Cheong Hee (Frontiers in Public Health, 2023)
The aim of this study is to analyse the performance of classifying stress and non-stress by measuring biosignal data using a wearable watch without interfering with work activities at work. An experiment is designed where participants wear a Galaxy Watch3 to measure HR and photoplethysmography data while performing stress-inducing and relaxation tasks. The classification model was constructed using k-NN, SVM, DT, LR, RF, and MLP classifiers. The performance of each classifier was evaluated using LOSO-CV as a verification method. When the top 9 features, including the average and minimum value of HR, average of NNI, SDNN, vLF, HF, LF, LF/HF ratio, and total power, were used in the classification model, it showed the best performance with an accuracy of 0.817 and an F1 score of 0.801. This study also finds that it is necessary to measure physiological data for more than 2 or 3 mins to accurately distinguish stress states.Recent studies have aimed to objectively quantify mental stress by analyzing physiological responses to stress using wearable sensors (4,5,6). Lee et al. measured Electrocardiogram (ECG) and Electroencephalogram (EEG) data while the participants played money games, and they analyzed the effects of stress on human physiological response (4). The ECG sensors were attached based on the