Performance of artificial intelligence models for monitoring psychological stress using noncontact physiological signals: a systematic review and meta-analysis
Abstract
Background: Psychological stress is a common and dynamic mental health concern, but conventional assessment methods often rely on self-report questionnaires, clinical interviews, or contact-based physiological sensors, which may limit continuous and unobtrusive monitoring in everyday settings. Noncontact sensing technologies combined with artificial intelligence (AI) provide a potential approach for low-burden assessment by capturing physiological or physiology-related information without direct body contact.
Objective: This systematic review and meta-analysis aimed to evaluate the performance of AI models using noncontact physiological signals for psychological stress recognition, summarize current sensing and modeling approaches, and identify methodological limitations affecting practical translation.
Methods: We searched PubMed, IEEE Xplore, Web of Science, Embase, and APA PsycINFO from inception to May 6, 2026, and performed backward citation searching. Eligible studies used noncontact sensing modalities, including remote or imaging photoplethysmography, thermal imaging, or wireless radar, combined with machine learning or deep learning methods to identify psychological stress. Narrative synthesis was conducted for all included studies, and quantitative meta-analysis was performed for binary stress classification tasks.
Results: Twenty-one studies were included, most of which were conducted in laboratory or controlled settings. For binary stress classification, 8 studies…
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