research-article

The improvement of hand gesture recognition based on sEMG by moving average filtering for features

Authors:

Jianpeng AnAuthors Info & Claims

AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture

Pages 2486 - 2491

https://doi.org/10.1145/3495018.3501126

Published: 14 March 2022 Publication History

Abstract

Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Hand gesture recognition based on sEMG plays an important role in the hand rehabilitation robotics. Improving the recognition accuracy of hand gesture is the goal always pursued by researchers. This study purposes to evaluate the performance of moving average filtering for features in improving the accuracy of hand gesture recognition based on sEMG. Firstly, empirical mode decomposition was applied on the effective motion segments extracted by sliding windows with different size and stride. Afterward, 21 features were extracted from original signal and each of the intrinsic mode function. Subsequently, moving average filtering with different orders were used to further suppress the noise in each feature series to improve the recognition performance. Finally, two representative machine learning methods (including decision tree and K nearest neighbor) were used to evaluate the performance. Compared with the original features, the intra-subject recognition accuracy rate after feature processing for KNN and DT increase by 17%, and 6%, respectively. The accuracy of the inter-subject across five subjects after feature processing increase by 28% (KNN), and 12% (DT), respectively. The above results prove that the moving average filtering for features could effectively improve the performance of hand gesture recognition.

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Cited By

Lim DKang HChoi BHong WLee J(2024)An Interpersonal Dynamics Analysis Procedure With Accurate Voice Activity Detection Using Low-Cost Recording SensorsIEEE Access10.1109/ACCESS.2024.338727912(68427-68440)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3387279
Chen CPeng FSun YLv DZhang NWang XWang L(2022)Epileptic Seizure Prediction Based on EEG by Auto-Machine Learning2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)10.1109/RCAR54675.2022.9872265(710-715)Online publication date: 17-Jul-2022
https://doi.org/10.1109/RCAR54675.2022.9872265

The improvement of hand gesture recognition based on sEMG by moving average filtering for features
1. Computing methodologies

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AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture

October 2021

3136 pages

ISBN:9781450385046

DOI:10.1145/3495018

Copyright © 2021 ACM.

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Published: 14 March 2022

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AIAM2021

AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture

October 23 - 25, 2021

Manchester, United Kingdom

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Cited By

Lim DKang HChoi BHong WLee J(2024)An Interpersonal Dynamics Analysis Procedure With Accurate Voice Activity Detection Using Low-Cost Recording SensorsIEEE Access10.1109/ACCESS.2024.338727912(68427-68440)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3387279
Chen CPeng FSun YLv DZhang NWang XWang L(2022)Epileptic Seizure Prediction Based on EEG by Auto-Machine Learning2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)10.1109/RCAR54675.2022.9872265(710-715)Online publication date: 17-Jul-2022
https://doi.org/10.1109/RCAR54675.2022.9872265

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