Investigating Functional Data Analysis for Wearable Physiological Sensor Data in Stress Evaluation

Measuring stress level objectively is crucial for personalized health monitoring. While traditional methods require a clinical setting, wearables provide a valuable alternative. In this paper, we approach stress assessment as a regression task, focusing on stress exposure, and evaluate Functional Data Analysis (FDA) to extract richer information from physiological signals. We apply scalar-on-function regression and functional clustering to WESAD, a public dataset which contains signals from wearables and psychometric questionnaires that we use as a ground truth for stress. We compare the results obtained by applying FDA with those achieved by methods using features extracted from signals rather than the signals themselves. The comparison reveals that FDA excels in capturing signal variations and their association with stress, offering new insights into how this association changes with different stressful activities. While non-functional techniques suffice for some analyses, FDA is key to capture overtime patterns linked to stress levels.