research-article

Lower Limb Motion Recognition by Integrating Multi-modal Features Based on Machine Learning Method

Authors:

Yongmei HuAuthors Info & Claims

CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering

Article No.: 135, Pages 1 - 6

https://doi.org/10.1145/3424978.3425120

Published: 20 October 2020 Publication History

Abstract

Various natural and man-made disasters such as wars, diseases and natural disasters have caused a large number of people to become physically disabled, while artificial limbs can help disabled patients to recover certain physical functions, so that they can integrate into society as ordinary people. One of the key techniques in the artificial limbs is the pattern recognition of the limb movement intention, which is still a challenging problem in relative researches. In this research, we proposed a reliable method by using multi-modal sources to recognize the intention of limb movement. First, we collected four signal sources which are EMG, acceleration, knee angles and foot pressure signal at various movement condition of the participant. Then we extract the relevant features from the four different signals. After that we used Relief-F to filter the multi-modal features to screen bad or redundant features. Finally, we compared the performance of three classifiers, which are LDA, SVM, LM-BP to find out the best algorithm to fit this problem. The result shows that when using the classifier of LDA, the average accuracy of movement intention recognition can reach up to 92.46%, while the time consuming is largely decreased compared to the other two models. The result revealed a feasible method to solve the problem of continuous recognition of the intention of limb movement.

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Cited By

Hayashida KNishikawa SKiguchi K(2022)Estimation of human intended motion and its phase for human-assist systems2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53654.2022.9945330(1531-1536)Online publication date: 9-Oct-2022
https://doi.org/10.1109/SMC53654.2022.9945330

Index Terms

Lower Limb Motion Recognition by Integrating Multi-modal Features Based on Machine Learning Method
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

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cover image ACM Other conferences

CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering

October 2020

1038 pages

ISBN:9781450377720

DOI:10.1145/3424978

Conference Chair:
Ali Emrouznejad,
Program Chair:
Jui-Sheng Rayson Chou

Copyright © 2020 ACM.

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Association for Computing Machinery

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Publication History

Published: 20 October 2020

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CSAE 2020

CSAE 2020: The 4th International Conference on Computer Science and Application Engineering

October 20 - 22, 2020

Sanya, China

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CSAE '20 Paper Acceptance Rate 179 of 387 submissions, 46%;

Overall Acceptance Rate 368 of 770 submissions, 48%

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Cited By

Hayashida KNishikawa SKiguchi K(2022)Estimation of human intended motion and its phase for human-assist systems2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53654.2022.9945330(1531-1536)Online publication date: 9-Oct-2022
https://doi.org/10.1109/SMC53654.2022.9945330

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