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@article{ART002952541},
author={Beom Kwon and Oh, Taegeun},
title={Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={4},
pages={41-51},
doi={10.9708/jksci.2023.28.04.041}

TY - JOUR
AU - Beom Kwon
AU - Oh, Taegeun
TI - Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 4
PB - The Korean Society Of Computer And Information
SP - 41
EP - 51
SN - 1598-849X
AB - 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.
KW - Anger Detection;Emotion Recognition;Ensemble Learning;Feature Extraction;Gait Data;Machine Learning
DO - 10.9708/jksci.2023.28.04.041
ER -

Beom Kwon and Oh, Taegeun. (2023). Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data. Journal of The Korea Society of Computer and Information, 28(4), 41-51.

Beom Kwon and Oh, Taegeun. 2023, "Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data", Journal of The Korea Society of Computer and Information, vol.28, no.4 pp.41-51. Available from: doi:10.9708/jksci.2023.28.04.041

Beom Kwon, Oh, Taegeun "Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data" Journal of The Korea Society of Computer and Information 28.4 pp.41-51 (2023) : 41.

Beom Kwon, Oh, Taegeun. Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data. 2023; 28(4), 41-51. Available from: doi:10.9708/jksci.2023.28.04.041

Beom Kwon and Oh, Taegeun. "Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data" Journal of The Korea Society of Computer and Information 28, no.4 (2023) : 41-51.doi: 10.9708/jksci.2023.28.04.041

Beom Kwon; Oh, Taegeun. Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data. Journal of The Korea Society of Computer and Information, 28(4), 41-51. doi: 10.9708/jksci.2023.28.04.041

Beom Kwon; Oh, Taegeun. Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data. Journal of The Korea Society of Computer and Information. 2023; 28(4) 41-51. doi: 10.9708/jksci.2023.28.04.041

Beom Kwon, Oh, Taegeun. Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data. 2023; 28(4), 41-51. Available from: doi:10.9708/jksci.2023.28.04.041

Beom Kwon and Oh, Taegeun. "Multi-Time Window Feature Extraction Technique for Anger Detection in Gait Data" Journal of The Korea Society of Computer and Information 28, no.4 (2023) : 41-51.doi: 10.9708/jksci.2023.28.04.041