A Feature Selection Method for Driver Stress Detection Using Heart Rate Variability and Breathing Rate

TL;DR

This paper introduces a feature selection approach using heart rate variability and breathing rate metrics, combined with SVM, to accurately detect driver stress, addressing safety and health concerns.

Contribution

It proposes a novel feature selection method based on minimal redundancy-maximal relevance for stress detection using physiological signals.

Findings

High accuracy in stress detection on the target dataset

Effective identification of optimal feature combinations

Reliable stress prediction with SVM

Abstract

Driver stress is a major cause of car accidents and death worldwide. Furthermore, persistent stress is a health problem, contributing to hypertension and other diseases of the cardiovascular system. Stress has a measurable impact on heart and breathing rates and stress levels can be inferred from such measurements. Galvanic skin response is a common test to measure the perspiration caused by both physiological and psychological stress, as well as extreme emotions. In this paper, galvanic skin response is used to estimate the ground truth stress levels. A feature selection technique based on the minimal redundancy-maximal relevance method is then applied to multiple heart rate variability and breathing rate metrics to identify a novel and optimal combination for use in detecting stress. The support vector machine algorithm with a radial basis function kernel was used along with these features to reliably predict stress. The proposed method has achieved a high level of accuracy on the target dataset.