一种基于心率变异性和呼吸率的驾驶员压力检测特征选择方法
A Feature Selection Method for Driver Stress Detection Using Heart Rate Variability and Breathing Rate
AI总结：
本文提出一种基于最小冗余-最大相关性特征选择的心率变异性和呼吸率组合方法，结合径向基函数核支持向量机，利用皮肤电反应估计真实压力，在目标数据集上实现高准确率检测驾驶员压力。
摘要：
驾驶员压力是全球车祸和死亡的主要原因之一。此外，持续的压力是一个健康问题，会导致高血压和其他心血管系统疾病。压力对心率和呼吸频率有可测量的影响，并且可以从这些测量中推断出压力水平。皮肤电反应是一种常见的测试，用于测量由生理和心理压力以及极端情绪引起的出汗。在本文中，皮肤电反应被用于估计真实压力水平。然后，基于最小冗余-最大相关性的特征选择技术被应用于多个心率变异性和呼吸率指标，以识别一种新颖且最优的组合用于压力检测。使用具有径向基函数核的支持向量机算法与这些特征一起可靠地预测压力。所提出的方法在目标数据集上实现了高准确率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
