Towards a Contactless Stress Classification Using Thermal Imaging

Machine learning-based classification analysis of knowledge worker mental stress

The aim of this study is to analyze 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 min to accurately distinguish stress states.

Classification of User Emotional Experiences on B2C Websites Utilizing Infrared Thermal Imaging

The acquisition of physiological signals for analyzing emotional experiences has been intrusive, and potentially yields inaccurate results. This study employed infrared thermal images (IRTIs), a noninvasive technique, to classify user emotional experiences while interacting with business-to-consumer (B2C) websites. By manipulating the usability and aesthetics of B2C websites, the facial thermal images of 24 participants were captured as they engaged with the different websites. Machine learning techniques were leveraged to classify their emotional experiences, with participants' self-assessments serving as the ground truth. The findings revealed significant fluctuations in emotional valence, while the participants' arousal levels remained consistent, enabling the categorization of emotional experiences into positive and negative states. The support vector machine (SVM) model performed well in distinguishing between baseline and emotional experiences. Furthermore, this study identified key regions of interest (ROIs) and effective classification features in machine learning. These findings not only established a significant connection between user emotional experiences and IRTIs but also broadened the research perspective on the utility of IRTIs in the field of emotion analysis.

Capacity of Autonomous Sensory Meridian Response on the Reduction of Mental Stress

In a social environment, various types of stress can be overwhelming. Humans frequently encounter these stressful situations in social life. Stress is divided into physical stress and mental stress; the latter is induced by heavy mental workloads and has become a huge social problem, leading to mental disorders and possibly suicide in the worst scenario. Investigations into monitoring mental stress and reducing stressful conditions are, therefore, important for its prevention. In the present study, we focused on autonomous sensory meridian response (ASMR) sound, which is known to improve the human mental condition through its comforting and relaxing effects. We investigated the effect of ASMR on the mental workload induced by mental tasks by the evaluation of EEG activation patterns in normal subjects. Our results showed a significant decrease in alpha-band activity and a significant increase in gamma (high beta)-band activity under the induction of mental workload by mental tasks compared to the resting condition. When applying ASMR sound, alpha- and gamma-band activity under the induction of mental workload by mental tasks was restored to the level of the resting condition. In conclusion, these results indicate that ASMR sound reduces the mental stress induced by mental workload.