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.

翻译：驾驶员压力是全球范围内导致交通事故和死亡的主要原因之一。此外，持续性压力是一种健康问题，会引发高血压及其他心血管系统疾病。压力对心率和呼吸频率具有可测量的影响，且压力水平可从这些测量数据中推断得出。皮肤电反应是一种常见的测试方法，用于测量由生理和心理压力以及极端情绪引起的出汗现象。本文利用皮肤电反应来估算压力水平的地面真值。随后，基于最小冗余-最大相关性方法，对多个心率变异性和呼吸率指标进行特征选择，以识别出一种新颖且最优的组合用于压力检测。采用径向基函数核的支持向量机算法，结合这些特征以实现可靠的压力预测。所提出的方法在目标数据集上实现了较高准确率。