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Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(4), pp.41-51
  • DOI : 10.9708/jksci.2023.28.04.041
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : April 3, 2023
  • Accepted : April 24, 2023
  • Published : April 28, 2023

Beom Kwon 1 Oh, Taegeun 2

1동양미래대학교
2동서울대학교

Accredited

ABSTRACT

In this paper, we propose a technique of multi-time window feature extraction for anger detection in gait data. In the previous gait-based emotion recognition methods, the pedestrian's stride, time taken for one stride, walking speed, and forward tilt angles of the neck and thorax are calculated. Then, minimum, mean, and maximum values are calculated for the entire interval to use them as features. However, each feature does not always change uniformly over the entire interval but sometimes changes locally. Therefore, we propose a multi-time window feature extraction technique that can extract both global and local features, from long-term to short-term. In addition, we also propose an ensemble model that consists of multiple classifiers. Each classifier is trained with features extracted from different multi-time windows. To verify the effectiveness of the proposed feature extraction technique and ensemble model, a public three-dimensional gait dataset was used. The simulation results demonstrate that the proposed ensemble model achieves the best performance compared to machine learning models trained with existing feature extraction techniques for four performance evaluation metrics.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.