Unsupervised Learning Method for Exploring Students' Mental Stress in Medical Simulation Training

So far, stress detection technology usually uses supervised learning methods combined with a series of physiological, physical, or behavioral signals and has achieved promising results. However, the problem of label collection such as the latency of stress response and subjective uncertainty introduced by the questionnaires has not been effectively solved. This paper proposes an unsupervised learning method with K-means clustering for exploring students' autonomic responses to medical simulation training in an ambulant environment. With the use of wearable sensors, features of electrodermal activity and heart rate variability of subjects are extracted to train the K-means model. The Silhouette Score of 0.49 with two clusters was reached, proving the difference in students' mental stress between baseline stage and simulation stage. Besides, with the aid of external ground truth which could be associated with either the baseline phase or simulation phase, four evaluation metrics were calculated and provided comparable results concerning supervised and unsupervised learning methods. The highest classification performance of 70% was reached with the measure of precision. In the future, we will integrate context information or facial image to provide more accurate stress detection.