What question did this study set out to answer?

This review aims to explore existing frameworks for detecting mental stress using physiological data and AI techniques.

March 6, 2026Sensors7 citationsOpen Access

Mental Stress Detection Using Physiological Sensors and Artificial Intelligence: A Review

RARabah Al AbdiAbu Dhabi University SAShouq AlKaabiAbu Dhabi University SEShada ElsifiAbu Dhabi University

Key Points

This review aims to explore existing frameworks for detecting mental stress using physiological data and AI techniques.
Review of various physiological sensors for continuous monitoring
Analysis of AI algorithms for interpreting stress data
Discussion of self-report instruments to benchmark sensor measurements
Examination of stress-inducing factors as reliable physiological triggers
Focus on wearable sensors for practical applications
Identified key physiological sensors used in mental stress detection
Highlighted the effectiveness of AI methods in classifying stress levels
Established a connection between physiological responses and self-report measures
Pointed out the need for advanced AI approaches in future research
Showed that current methods provide significant data for accurate stress assessment

Abstract

Stress can cause many disorders, including mental and physical ones, if it persists. To take timely and effective early intervention measures, mental stress levels must be carefully monitored. This study investigates the rapidly growing topic of mental stress detection, focusing on the primary goals and mechanisms of existing detection frameworks. The main objectives and mechanisms will be highlighted. This study examines physiological sensors, stressors, algorithms, monitoring methods, and validation tools used to assess and classify mental stress. The study targets physiological sensors. Wearable sensors are becoming more popular because they can continuously monitor physiological responses in human-like environments. This allows them to reveal relevant stress patterns across various work environments. Numerous physiological sensors are used regularly. Galvanic skin response (GSR), electrocardiogram (ECG), photoplethysmography (PPG), electroencephalography (EEG), and pupil diameter camera systems are examples of these sensors. The combination of these sensors provides a wealth of cognitive and autonomic response data for stress detection. This review examines AI-based methods for interpreting complex physiological data. Machine learning and ensemble models are emphasized for improving stress classification accuracy and reducing incorrect classifications. In addition, this article discusses stressors used to induce reliable physiological responses. Validated self-report instruments are being reviewed as benchmarking tools for objective sensor-based measurements. STAI and PSS-10 are examples. These instruments demonstrate a strong correlation between stress and anxiety and physiological health outcomes. In conclusion, this review discusses future research avenues, focusing on advanced artificial intelligence-driven approaches and sophisticated sensors. These developments aim to better define stress levels and physiological factors that have not been thoroughly studied.

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