A feature selection method for driver stress detection using heart rate variability and breathing rate

Ashkan Parsi; David O'Callaghan; Joseph Lemley

doi:10.1117/12.2680547

7 June 2023 A feature selection method for driver stress detection using heart rate variability and breathing rate

Ashkan Parsi, David O'Callaghan, Joseph Lemley

Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 127010Y (2023) https://doi.org/10.1117/12.2680547
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

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.

Citation Download Citation

Ashkan Parsi, David O'Callaghan, and Joseph Lemley "A feature selection method for driver stress detection using heart rate variability and breathing rate", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 127010Y (7 June 2023); https://doi.org/10.1117/12.2680547

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