Machine Learning and Deep Learning Techniques to Detect Mental Stress Using Various Physiological Signals: A Critical Insight

ABSTRACT This paper presents a comprehensive review on the various techniques and methodologies employed to detect stress among individuals. The review encompasses a broad spectrum of methods, including physiological measurements, wearable technology, machine learning and deep learning algorithms, and contactless image‐based techniques. The paper outlines the physiological markers commonly associated with stress, such as Electrocardiogram (ECG), Electroencephalography (EEG), Photoplethysmography (PPG), and Skin Galvanic response. It examines the various wearable and contactless techniques to acquire data. Furthermore, it explores the integration of machine learning and deep learning techniques for the development of predictive stress detection models, highlighting their accuracy. It also addresses the potential of multispectral and hyperspectral imaging in this area. Some of the publicly available datasets are also discussed in this paper. This article is categorized under: Application Areas > Health Care Technologies > Machine Learning