Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Motion classification using distributional features of 3D skeleton data

  • Woohyun Kim (Department of Statistics, Dankook University) ;
  • Daeun Kim (Department of Statistics, Dankook University) ;
  • Kyoung Shin Park (Department of Computer Engineering, Dankook University) ;
  • Sungim Lee (Department of Statistics, Dankook University)
  • Received : 2023.02.15
  • Accepted : 2023.09.14
  • Published : 2023.11.30
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Abstract

Recently, there has been significant research into the recognition of human activities using three-dimensional sequential skeleton data captured by the Kinect depth sensor. Many of these studies employ deep learning models. This study introduces a novel feature selection method for this data and analyzes it using machine learning models. Due to the high-dimensional nature of the original Kinect data, effective feature extraction methods are required to address the classification challenge. In this research, we propose using the first four moments as predictors to represent the distribution of joint sequences and evaluate their effectiveness using two datasets: The exergame dataset, consisting of three activities, and the MSR daily activity dataset, composed of ten activities. The results show that the accuracy of our approach outperforms existing methods on average across different classifiers.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1003257).

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