Enhancement of Physiological Stress Classification using Psychometric Features

Psychological data features are underutilized in many acute stress studies since they are challenging to replicate and validate due to their inherent subjectivity. However, psychology and perception play essential roles in stress research according to the well-established allostatic load model. Therefore, we demonstrate the importance of accounting for psychological data in acute stress research in an ambulatory setting through a joint analysis. We enhanced stress classification by combining psychometric features with standard physiological signal features. We used the publicly available Wearable Stress and Affect Database (WESAD), from which we obtained physiological signals and psychological self-assessments from 15 participants. For each participant, a set of physiologically relevant features were extracted from each signal type. In parallel, we adapted psychometric features, positive emotion (PEscore) and negative emotion (NEscore) scores, by calculating the weighted average of self-evaluation scores. Using a stepwise feature selection and a linear-discriminant-analysis-based classifier, we found that PEscore along with select physiological signal features, could enhance cross-validated stress classification accuracy by 8%, higher than a previous benchmark study using the same dataset. More importantly, we found that such a classification accuracy could be achieved with significantly fewer physiological signal features (by 20 times) with the aid of a psychometric feature. Finally, we found that psychometric features could indicate the type of perceived stress relating to an individual's mood descriptor scores. Thus. a combination of psychometric and physiological data could be beneficial towards improving the detection and management of stress and support the development of holistic stress models.