Review on Machine Learning Frameworks in Drivers’ Physiological Signal Analysis to Detect Stress

Abstract

Nowadays, the performance of classification methods used in physiological signals for healthcare purposes governs computer-based analysis. A prevalent research area in biosignal analysis with the aim of stress recognition and classification is to design algorithms that outperform established approaches. Accurate stress realization in drivers would address significant costs of driving-induced stress and increase drivers' safety if unobstructed and fully automated stress detection devices developed. This procedure faces some challenges such as human stress detection and recognition, along with simulating stress in computing devices. For stress computing, the initial step is to detect stress which requires capturing human state data. The next step is to extract the most relevant features to find meaningful patterns to analyze data which could be performed by mathematical analysis and machine learning tools. In this paper, a review of the recent advancement in signal processing, feature extraction, and machine learning methods with a focus on galvanic skin response (GSR) analysis is presented. This review enlightens the reader with common methodologies, issues, and future opportunities in this research area.