Gait Type Classification Using Multi-modal Ensemble Deep Learning Network (멀티모달 앙상블 딥러닝 네트워크를 이용한 보행 타입 분류)

Hee-Chan Park (박희찬); Young-Chan Choi (최영찬); Sang-Il Choi (최상일)

doi:10.9708/jksci.2022.27.11.029

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%.

KEYWORDS

Gait type, Deep learning, Ensemble network, Smart insole, Multi-modal sensor

Citation status

* References for papers published after 2023 are currently being built.

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

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Gait Type Classification Using Multi-modal Ensemble Deep Learning Network

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Gait Type Classification Using Multi-modal Ensemble Deep Learning Network

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