Subspace-based feature extraction on multi-physiological measurements of automobile drivers for distress recognition. (April 2021)

• Record Type: Journal Article
• Authors: Isikli Esener, Idil
• Is Part Of: Biomedical signal processing and control. Volume 66(2021)
• Publication Date: 2021-04
• Subjects: Stress recognition -- Intelligent transport systems -- Discriminative common vector -- Support vector machines; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102504
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A novel feature extraction scheme for drivers' stress level detection is proposed. Subspace decomposition of the features is realized in the proposed scheme. An accuracy of 100 % is attained by the proposed scheme. 100 % accuracy is achieved by using only 2-dimensional features by the proposed scheme. Abstract: The automotive industry has accelerated the utilization of Intelligent Transport Systems (ITS) in vehicles for increased driving safety. In this paper, a novel and well-done subspace feature extraction scheme on the physiological signals acquired by wearable sensors, for drivers' distress level detection to be introduced as an ITS is proposed and verified on the publicly available MIT-BIH PhysioNet Multi-parameter Database. The proposed scheme includes two phases where time-domain statistical feature extraction is first realized on the electrocardiogram (ECG), hand galvanic skin response (hand GSR), foot galvanic skin response (foot GSR), electromyogram (EMG), and respiration (RESP) signals, and secondly subspace feature vector construction is appreciated by applying Discriminative Common Vector (DCV) decomposition on the statistical feature vectors. The distress levels of the drivers are determined as low, moderate, and high by utilizing both the statistical and the subspace feature vectors using Support Vector Machines (SVM) classifier by 2-fold cross-validation technique. A maximum of 88.89 % classification accuracy is achieved using statistical features in 7384 s while it is increased to 100 % in 3, 421 s when subspace features are employed. The increased classification accuracy in decreased time consumption evidently shows the success of the proposed feature extraction scheme.