Driving conditions recognition using heart rate variability indexes

This study presents a physiological recognition strategy based on HRV-parameter-based recognition strategy. The strategy consists of the following processes: 1) feature generation, 2) feature selection, 3) feature extraction, and 4) classifier construction for recognition. In the feature generation processes, the parameter-based strategy calculates features from five-minute HRV analysis results. In the feature selection process, the strategy adopts the best individual N (BIN) as the search strategy and the kernel-based class separability (KBCS) as the selection criterion. Sequentially, principal component analysis (PCA) and linear discriminant analysis (LDA) are adopted in the feature extraction process. Finally, a k-nearest neighbor (k-NN) algorithm is used for the recognition. The feasibility of the recognition strategy is verified by driving condition recognition. The simulation results demonstrate that the proposed strategy can achieve satisfactory recognition rates for recognizing driving conditions. The results show that the feature extraction process or feature selection process has respective physical meaning in the proposed strategies.