Unsupervised Machine Learning Approach for Stress Level Classification Using Electrodermal Activity Signals

Mental Stress has become a heightened concern in recent times. Stress creates physiological shifts that manifest within the realms of the human physiological system. Biosignals are considered realistic biomarkers for measuring an individual's emotional state. Among the various physiological signals considered in the study of Stress, a negative emotion, Electrodermal Activity (EDA), stands out as a promising BioSignal measuring the electrical properties of the skin, which is directly or indirectly related to emotional arousal. Six Time Domain Features are extracted further. Unsupervised Machine learning techniques such as K-means clustering are employed to label stressed EDA data into three Stress states: 'Low,' 'Moderate,' and 'High.' Six different classifiers are used to check the classification accuracy of the three stress levels. The Decision Tree achieved the highest precision rate, followed by 93% accuracy with random forest and Naive Bayes and Support Vector Machine with 86% accuracy. Through the lens of EDA, this study delves into a better understanding of patterns of Stress, revealing its physiological underpinnings to contribute to a deeper insight into human well-being.