Driving conditions recognition using heart rate variability indexes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2010 6th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2010
Pages389-392
Number of pages4
DOIs
Publication statusPublished - 2010 Dec 28
Event6th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2010 - Darmstadt, Germany
Duration: 2010 Oct 152010 Oct 17

Publication series

NameProceedings - 2010 6th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2010

Other

Other6th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2010
CountryGermany
CityDarmstadt
Period10-10-1510-10-17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Signal Processing

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