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Gait Type Classification Using Multi-modal Ensemble Deep Learning Network

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(11), pp.29-38
  • DOI : 10.9708/jksci.2022.27.11.029
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 30, 2022
  • Accepted : November 15, 2022
  • Published : November 30, 2022

Hee-Chan Park 1 Young-Chan Choi 2 Sang-Il Choi 2

1단국대학교, 알체라
2단국대학교

Accredited

ABSTRACT

This paper proposes a system for classifying gait types using an ensemble deep learning network for gait data measured by a smart insole equipped with multi-sensors. The gait type classification system consists of a part for normalizing the data measured by the insole, a part for extracting gait features using a deep learning network, and a part for classifying the gait type by inputting the extracted features. Two kinds of gait feature maps were extracted by independently learning networks based on CNNs and LSTMs with different characteristics. The final ensemble network classification results were obtained by combining the classification results. For the seven types of gait for adults in their 20s and 30s: walking, running, fast walking, going up and down stairs, and going up and down hills, multi-sensor data was classified into a proposed ensemble network. As a result, it was confirmed that the classification rate was higher than 90%.

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.