research-article

Intelligent Perception Recognition of Multi-modal EMG Signals Based on Machine Learning

Authors:

Mingchuan Zhang,

Guannan MengAuthors Info & Claims

BIC '22: Proceedings of the 2022 2nd International Conference on Bioinformatics and Intelligent Computing

Pages 389 - 396

https://doi.org/10.1145/3523286.3524576

Published: 31 May 2022 Publication History

Abstract

Surface Electromyography (sEMG) signals directly and objectively reflect the activity of human muscles. As a convenient non-invasive EMG detection method, it is widely used in the field of human action recognition. First, this paper performs active segment detection on the sEMG data collected by the MYO bracelet to extract effective active segments. Subsequently, we extracted five time-domain features from the active segment signal, including the root mean square value, the length of the waveform, the number of zero-crossing points, the mean absolute value, and the maximum-minimum value. Four classifiers, i.e, K nearest neighbor (KNN), support vector machine (SVM), decision tree (DT) and random forest (RF) are used to classify and recognize the extracted sEMG. The highest correct rate is random forest with a value of 82%. Therefore, this paper further extracts the frequency domain characteristics of the signal including the Fourier transform and Willison amplitude. We added 4 models for comparative experiments, including gradient boosting (GB), Gaussian Naive Bayes (NB), linear discriminant analysis (LDA) and logistic regression (LR). The final experimental conclusion show that the effects of the four classifiers have been significantly improved. The best result is SVM for intelligent perception recognition of multi-modal EMG signals, with an accuracy rate of 90% and an F1 score of 0.87.

References

[1]

Nagaswathi Amancherla, Anish Turlapaty, and Balakrishna Gokaraju. 2019. SVM based Classification Of sEMG Signals using Time Domain Features for the Applications towards Arm Exoskeletons. In Proceedings of the Advances in Robotics 2019 (AIR 2019). Association for Computing Machinery, New York, NY, USA, Article 26, 1–5.

Digital Library

[2]

Q. Ding, J. Han, X. Zhao and Y. Chen, "Missing-Data Classification With the Extended Full-Dimensional Gaussian Mixture Model: Applications to EMG-Based Motion Recognition," in IEEE Transactions on Industrial Electronics, vol. 62, no. 8, pp. 4994-5005, 2015.

[3]

P. McCool, G. D. Fraser, A. D. C. Chan, L. Petropoulakis and J. J. Soraghan, "Identification of Contaminant Type in Surface Electromyography (EMG) Signals," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 4, pp. 774-783, July 2014.

[4]

P. Geethanjali and K. K. Ray, "A Low-Cost Real-Time Research Platform for EMG Pattern Recognition-Based Prosthetic Hand," in IEEE/ASME Transactions on Mechatronics, vol. 20, no. 4, pp. 1948-1955, Aug. 2015.

[5]

Min Lei and Guang Meng. 2011. Classification of the action surface EMG signals based on the dirichlet process mixtures method. In Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I (ICIRA'11). Springer-Verlag, Berlin, Heidelberg, 212–220.

Digital Library

[6]

Kyung Kwon Jung, Joo Woong Kim, Hyun Kwan Lee, Sung Boo Chung and Ki Hwan Eom, "EMG pattern classification using spectral estimation and neural network," SICE Annual Conference 2007, 2007, pp. 1108-1111.

[7]

N. Bu, M. Okamoto and T. Tsuji, "A Hybrid Motion Classification Approach for EMG-Based Human–Robot Interfaces Using Bayesian and Neural Networks," in IEEE Transactions on Robotics, vol. 25, no. 3, pp. 502-511, June 2009.

Digital Library

[8]

A. B. M. S. U. Doulah, S. A. Fattah, W. -. Zhu and M. O. Ahmad, "Wavelet Domain Feature Extraction Scheme Based on Dominant Motor Unit Action Potential of EMG Signal for Neuromuscular Disease Classification," in IEEE Transactions on Biomedical Circuits and Systems, vol. 8, no. 2, pp. 155-164, April 2014.

[9]

T. Kamali and D. W. Stashuk, "Transparent Electrophysiological Muscle Classification From EMG Signals Using Fuzzy-Based Multiple Instance Learning," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 4, pp. 842-849, April 2020.

[10]

Wei W T, Wong Y K, Du Y, A multi-stream convolutional neural network for sEMG-based gesture recognition in musclecomputer interface. Pattern Recognition Letters, 2019, 119: 131-138.

Digital Library

[11]

P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, "Improved Gesture Recognition Based on sEMG Signals and TCN," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 1169-1173.

[12]

Lobov S., Krilova N., Kastalskiy I., Kazantsev V., Makarov V.A., Latent Factors Limiting the Performance of sEMG-Interfaces. Sensors. 2018;18(4): 1122.

[13]

X. Wu, W. Zuo, L. Lin, W. Jia and D. Zhang, "F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation," in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 11, pp. 5185-5199, Nov. 2018.

[14]

N. Qu, J. Zuo, J. Chen and Z. Li, "Series Arc Fault Detection of Indoor Power Distribution System Based on LVQ-NN and PSO-SVM," in IEEE Access, vol. 7, pp. 184020-184028, 2019.

[15]

J. Wu and H. Yang, "Linear Regression-Based Efficient SVM Learning for Large-Scale Classification," in IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 10, pp. 2357-2369, Oct. 2015.

[16]

Z. Li, J. Zuo, J. Wu, Z. Liu, Y. Song, and S. Geng. 2021. Application of Decision Tree Integrated Hybrid Classifier in Feature-Fused Robot Big Data. In Proceedings of the 2021 International Conference on Control and Intelligent Robotics (ICCIR 2021). Association for Computing Machinery, New York, NY, USA, 681–686.

Digital Library

[17]

L. Liao, Y. Tseng, H. Chiang and W. Wang, "EMG-based Control Scheme with SVM Classifier for Assistive Robot Arm," 2018 International Automatic Control Conference (CACS), 2018, pp. 1-5.

[18]

C. Hachemi, M. K. Talha, H. Zairi and K. Meddah, "Single channel EMG classification using DWT and SVM," 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), 2021, pp. 199-203.

Cited By

Su HQi WChen JYang CSandoval JLaribi M(2023)Recent advancements in multimodal human–robot interactionFrontiers in Neurorobotics10.3389/fnbot.2023.108400017Online publication date: 11-May-2023
https://doi.org/10.3389/fnbot.2023.1084000
Eddy EScheme EBateman S(2023)A Framework and Call to Action for the Future Development of EMG-Based Input in HCIProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580962(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580962
Dhumal SSharma P(2023)Performance Assessment of Machine Learning Algorithms and Ensemble Techniques for Hand Gesture Recognition using Electromyographic Signals2023 IEEE International Conference on Contemporary Computing and Communications (InC4)10.1109/InC457730.2023.10263039(1-6)Online publication date: 21-Apr-2023
https://doi.org/10.1109/InC457730.2023.10263039

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BIC '22: Proceedings of the 2022 2nd International Conference on Bioinformatics and Intelligent Computing

January 2022

551 pages

ISBN:9781450395755

DOI:10.1145/3523286

Copyright © 2022 ACM.

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Published: 31 May 2022

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BIC 2022

BIC 2022: 2022 2nd International Conference on Bioinformatics and Intelligent Computing

January 21 - 23, 2022

Harbin, China

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Cited By

Su HQi WChen JYang CSandoval JLaribi M(2023)Recent advancements in multimodal human–robot interactionFrontiers in Neurorobotics10.3389/fnbot.2023.108400017Online publication date: 11-May-2023
https://doi.org/10.3389/fnbot.2023.1084000
Eddy EScheme EBateman S(2023)A Framework and Call to Action for the Future Development of EMG-Based Input in HCIProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580962(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580962
Dhumal SSharma P(2023)Performance Assessment of Machine Learning Algorithms and Ensemble Techniques for Hand Gesture Recognition using Electromyographic Signals2023 IEEE International Conference on Contemporary Computing and Communications (InC4)10.1109/InC457730.2023.10263039(1-6)Online publication date: 21-Apr-2023
https://doi.org/10.1109/InC457730.2023.10263039

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