Comparative Analysis of Physiological and Speech Signals for State Anxiety Detection in University Students in STEM

Anxiety is a common mental health condition that can significantly impair daily functioning, especially for university students in STEM. State anxiety is a situational emotional response and is typically assessed through self-reported questionnaires and clinical interviews. These traditional methods only capture discrete snapshots of an individual’s emotional state and rely heavily on retrospective reporting. To overcome the limitations of self-reporting, we use wearable and contactless sensors. We continuously monitor a set of physiological signals (electrodermal activity (EDA), blood volume pulse (BVP), heart rate variability (HRV), and skin temperature (TEMP)) along with a behavioral signal (Speech) to detect state anxiety in real-time. We evaluate the predictive capabilities of these signals concerning self-reported anxiety levels, as measured by the six-item StateTrait Anxiety Inventory (STAI-6). Machine learning (ML) models are employed to classify participants into state anxiety risk groups based on two thresholds: clinical (STAI-6 score > 15) and median-based (STAI-6 score > median). Our results indicate that BVP outperforms other single modalities across classifiers, particularly when combined with TEMP, achieving true positive rates (TPRs) up to 0.90 under the median threshold. Additionally, speech features demonstrate competitive performance in certain conditions, while EDA, TEMP, and HRV exhibit greater variability. We believe that this study supports using wearables to monitor state anxiety.