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

Autor:	Parsi, Ashkan, O'Callaghan, David, Lemley, Joseph
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Computer Science - Human-Computer Interaction
Druh dokumentu:	Working Paper
Popis:	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. Comment: In Proceedings of the 15th International Conference on Machine Vision (ICMV), Rome, Italy, 18-20 November 2022. arXiv admin note: text overlap with arXiv:2206.03222
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.01602 Zobrazit plný text záznamu View this record from Arxiv