Assessment of anxiety detection in students using machine learning and wearable sensors during virtual and in-person laboratory sessions

A pilot study investigated the feasibility of using multimodal physiological monitoring, including electroencephalographic (EEG) and peripheral signals, combined with machine learning to detect anxiety in graduate engineering students during virtual and in-person laboratory sessions. This approach aims to provide objective, participant-level anxiety screening to complement traditional self-report assessments, addressing the growing concern of anxiety among graduate students.

The development of objective anxiety detection methods using technology has significant strategic importance as it can provide early intervention opportunities and support student well-being. Proactive identification of anxiety can enhance educational outcomes, reduce attrition, and improve the overall mental health landscape within academic institutions.

Anxiety is an increasing concern among graduate students, necessitating objective assessment methods.

Forward consideration, not a verified fact.