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.

References

[1]

Bethge M, von Groote P, Giustini A, Gutenbrunner C. The World Report on Disability: a challenge for rehabilitation medicine[J]. Am J Phys Med Rehabil. 2014;93(1 Suppl 1):S4-11.

[2]

Lu L. A Research on Social Support System and Its Optimization for the disabled [D]: University of Electronic Science and Technology of China; 2019.

[3]

Kalani H, Moghimi S, Akbarzadeh A. Towards an SEMG-based tele-operated robot for masticatory rehabilitation[J]. COMPUT BIOL MED. 2016;75:243-56.

[4]

Geiger DE, Behrendt F, Schuster-Amft C. EMG Muscle Activation Pattern of Four Lower Extremity Muscles during Stair Climbing, Motor Imagery, and Robot-Assisted Stepping: A Cross-Sectional Study in Healthy Individuals[J]. BIOMED RES INT. 2019;2019:9351689.

[5]

Liparulo L, Zhang Z, Panella M, Gu X, Fang Q. A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography [J]. MED BIOL ENG COMPUT. 2017; 55(8): 1367-78.

[6]

Salchow C, Dorn A, Valtin M, Schauer T. Intention recognition for FES in a grasp-and-release task using volitional EMG and inertial sensors[J]. Current directions in biomedical engineering. 2017;3(2):161-5.

[7]

Cesqui B, Tropea P, Micera S, Krebs HI. EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study[J]. J NEUROENG REHABIL. 2013;10:75.

[8]

Chowdhury RH, Reaz MB, Ali MA, Bakar AA, Chellappan K, Chang TG. Surface electromyography signal processing and classification techniques[J]. Sensors (Basel). 2013;13(9):12431-66.

[9]

Bai D, Chen S, Yang J. Upper Arm Motion High-Density sEMG Recognition Optimization Based on Spatial and Time-Frequency Domain Features[J]. J HEALTHC ENG. 2019;2019:3958029.

[10]

Crepin R, Fall CL, Mascret Q, Gosselin C, Campeau-Lecours A, Gosselin B. Real-Time Hand Motion Recognition Using sEMG Patterns Classification[J]. Annu Int Conf IEEE Eng Med Biol Soc. 2018; 2018: 2655-8.

[11]

Hu Y, Wong Y, Wei W, Du Y, Kankanhalli M, Geng W. A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition[J]. PLOS ONE. 2018;13(10):e206049.

[12]

He Y, Fukuda O, Bu N, Okumura H, Yamaguchi N. Surface EMG Pattern Recognition Using Long Short-Term Memory Combined with Multilayer Perceptron[J]. Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:5636-9.

[13]

Sun W, Liu H, Tang R, Lang Y, He J, Huang Q. sEMG-Based Hand-Gesture Classification Using a Generative Flow Model. Sensors (Basel). 2019 Apr 25;19(8):1952.

[14]

Tam S, Boukadoum M, Campeau-Lecours A, Gosselin B. A Fully Embedded Adaptive Real-Time Hand Gesture Classifier Leveraging HD-sEMG and Deep Learning. IEEE Trans Biomed Circuits Syst. 2020 Apr;14(2):232-243.

[15]

St GL, Hobbs SJ, Richards J, Sinclair J, Holt D, Roy SH. The effect of cut-off frequency when high-pass filtering equine sEMG signals during locomotion[J]. J Electromyogr Kinesiol. 2018;43:28-40.

[16]

Papagiannis GI, Triantafyllou AI, Roumpelakis IM, Zampeli F, Garyfallia EP, Koulouvaris P, Papadopoulos EC, Papagelopoulos PJ. Methodology of surface electromyography in gait analysis: review of the literature[J]. J Med Eng Technol. 2019;43(1):59-65.

[17]

Liu SH, Chang KM, Cheng DC. The progression of muscle fatigue during exercise estimation with the aid of high-frequency component parameters derived from ensemble empirical mode decomposition[J]. IEEE J Biomed Health Inform. 2014;18(5):1647-58.

[18]

Pilkar R, Ramanujam A, Garbarini E, Forrest G. Validation of empirical mode decomposition combined with notch filtering to extract electrical stimulation artifact from surface electromyograms during functional electrical stimulation[J]. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:1733-6.

[19]

St GL, Hobbs SJ, Richards J, Sinclair J, Holt D, Roy SH. The effect of cut-off frequency when high-pass filtering equine sEMG signals during locomotion[J]. J Electromyogr Kinesiol. 2018;43:28-40.

[20]

Sapsanis C, Georgoulas G, Tzes A, Lymberopoulos D. Improving EMG based classification of basic hand movements using EMD[J]. Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5754-7.

[21]

Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N, Tung CC. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society. A, Mathematical, physical, and engineering sciences. 1998;454(1971):903-95.

[22]

Englehart K, Hudgins B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng. 2003 Jul;50(7):848-54.

[23]

Wen T, Zhang Z, Qiu M, Zeng M, Luo W. A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN. J Xray Sci Technol. 2017;25(2):287-300.

[24]

Phinyomark A., Phukpattaranont P., Limsakul C. Feature reduction and selection for EMG signal classification. Expert Syst. Appl. 2012; 39:7420–7431.

[25]

Chen L, Fu J, Wu Y, Li H, Zheng B. Hand Gesture Recognition Using Compact CNN Via Surface Electromyography Signals. Sensors (Basel). 2020;20(3):672.

[26]

Guo S, Pang M, Gao B, Hirata H, Ishihara H. Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement[J]. Sensors (Basel). 2015;15(4):9022-38.

[27]

Shahzad W, Ayaz Y, Khan MJ, Naseer N, Khan M. Enhanced Performance for Multi-Forearm Movement Decoding Using Hybrid IMU-sEMG Interface[J]. Front Neurorobot. 2019;13:43.

[28]

Tosin MC, Majolo M, Chedid R, Cene VH, Balbinot A. sEMG feature selection and classification using SVM-RFE[J]. Annu Int Conf IEEE Eng Med Biol Soc. 2017;2017:390-3.

[29]

Wen T, Zhang Z, Qiu M, Zeng M, Luo W. A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN. J Xray Sci Technol. 2017;25(2):287-300. 28269818.

[30]

Song W, Han Q, Lin Z, Yan N, Luo D, Liao Y, Zhang M, Wang Z, Xie X, Wang A, Chen Y, Bai S. Design of a Flexible Wearable Smart sEMG Recorder Integrated Gradient Boosting Decision Tree Based Hand Gesture Recognition. IEEE Trans Biomed Circuits Syst. 2019 Dec;13(6):1563-1574.

[31]

Chowdhury SK, Nimbarte AD, Jaridi M, Creese RC. Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles. J Electromyogr Kinesiol. 2013 Oct;23(5):995-1003.

[32]

Lienhard K, Cabasson A, Meste O, Colson SS. Comparison of sEMG processing methods during whole-body vibration exercise. J Electromyogr Kinesiol. 2015 Dec;25(6):833-40.

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

Recommendations

Depth-based hand gesture recognition

In this article, a dynamic gesture recognition system with the depth information is proposed. The proposed system consists of three main components: preprocessing, static posture recognition and dynamic gesture recognition. In the first component, the ...
Vision-Based Hand Gesture Recognition Using Combinational Features
IIH-MSP '10: Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing

This paper presents a feature extraction method for hand gesture based on multi-layer perceptron. The feature of hand skin color in the YCbCr color space is used to detect hand gesture. The hand silhouette and features can be accurately extracted in ...
Shape-Based Features for Optimized Hand Gesture Recognition

Gesture recognition is the most intuitive form of human-computer interface. Hand gestures provide a natural way for humans to interact with computers to perform a variety of different applications. However, factors such as complexity of hand gesture ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

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

October 2021

3136 pages

ISBN:9781450385046

DOI:10.1145/3495018

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Research
Refereed limited

Conference

AIAM2021

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

October 23 - 25, 2021

Manchester, United Kingdom

Acceptance Rates

Overall Acceptance Rate 100 of 285 submissions, 35%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
61
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)2

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

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

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents