Control of a robot manipulator using surface electromyography signals
DOI:
https://doi.org/10.30837/2522-9818.2025.3.203Keywords:
electromyography; gesture control; robotic manipulator; convolutional neural network; ResNet; data normalization; bionic prostheses; real-time control.Abstract
The subject of the study is the methods of controlling a robotic manipulator based on surface electromyography (EMG) signals using individualized normalization and classification of gestures by a convolutional neural network (CNN). The aim of the work is to create an effective control system that combines pre-normalization of EMG signals and deep learning to increase the accuracy of gesture recognition and stability in real time. For this purpose, the following were used: signal collection using the Myo Armband, pre-processing (min-max and zero-mean), signal conversion into images through a sliding window, CNN training (ResNet, Adam optimization) and comparison with SVM and Random Forest models. The results obtained showed a classification accuracy of 97.27% in the test environment and 91.71% in real time. The CNN model outperformed traditional methods by 18–19%, and zero-mean increased the accuracy by 2.34% compared to min-max normalization. The system remained stable even with variations in the bracelet position due to individual normalization. Conclusions: the proposed system demonstrates high accuracy, reliability and adaptability in real time. The scientific novelty lies in the combination of individualized normalization of EMG signals with ResNet, which provides stability and exceeds the accuracy of traditional algorithms. In the future, it is planned to expand the set of gestures, study more complex conditions and optimize neural networks for embedded systems.
References
Список літератури
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References
Guo, B., Ma, Y., Yang, J., Wang, Z., Zhang, X. (2020), "Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation". Computational Intelligence and Neuroscience, 2020, 8846021 р. DOI: https://doi.org/10.1155/2020/8846021
Wan, Y., Han, Z., Zhong, J., & Chen, G. (2018), "Pattern recognition and bionic manipulator driving by surface electromyography signals using convolutional neural network". Advances in Mechanical Engineering, Vol. 10(10), Р. 1–11. DOI: https://doi.org/10.1177/1729881418802138
Bao, T., Zaidi, S. A. R., Xie, S., Yang, P., Zhang, Z. (2019), "A CNN-LSTM Hybrid Framework for Wrist Kinematics Estimation Using Surface Electromyography". IEEE Transactions on Instrumentation and Measurement. 82 р. 2019. DOI: https://doi.org/10.1109/TIM.2020.3036654
"Real-time Bionic Arm Control Via CNN-based EMG Recognition". available at: https://www.hackster.io/emgarm/real-time-bionic-arm-control-via-cnn-based-emg-recognition-b013d3
Meng, Q., Yue, Y., Li, S., Yu, H. (2022), "Electromyogram-based motion compensation control for the upper limb rehabilitation robot in active training". Mechanical Sciences, 13(2), Р. 675–685. DOI: https://doi.org/10.5194/ms-13-675-2022
Atzori, M., Gijsberts, A., Castellini, C., Caputo, B., Mittaz Hager, A.G., Elsig, S., Giatsidis, G., Bassetto, F., Müller, H. (2014), "Electromyography data for non-invasive naturally-controlled robotic hand prostheses". Scientific Data, № 1, 140053 р. DOI: https://doi.org/10.1038/sdata.2014.53
Banzi, M., Shiloh, M. (2014), "Getting Started with Arduino: The Open Source Electronics Prototyping Platform. Maker Media", available at: https://www.sarcnet.org/files/Getting%20Started%20With%20Arduino.pdf
Patel, S., Patel, V. (2018), "A study on normalization techniques in data mining". International Journal of Computer Applications, № 179(14), Р. 1–4. DOI: https://doi.org/10.5120/ijca2018917759
Bhandari, P. (2020), "The Standard Normal Distribution | Calculator, Examples & Uses. Scribbr". available at: https://www.scribbr.com/statistics/standard-normal-distribution/
He, K., Zhang, X., Ren, S., Sun, J. (2016), "Deep residual learning for image recognition". In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Р. 770-778. DOI: https://doi.org/10.1109/CVPR.2016.90
Kingma, D. P., Ba, J. (2014), "Adam: A method for stochastic optimization". In Proceedings of the International Conference on Learning Representations (ICLR). DOI: https://doi.org/10.48550/arXiv.1412.6980
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D., Steiner, B., Zheng, X. (2016), "TensorFlow: A system for large-scale machine learning". In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Р. 265-283. available at: https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi
Dianchun, Bai, Tie, Liu, Xinghua, Han, Hongyu, Yi. (2021), "Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM". Cyborg and Bionic Systems. Vol 2021. 2021. DOI:10.34133/2021/9794610
Silva, А.D., Perera, M.V., Wickramasinghe, K., Naim, A.M., Dulantha, Lalitharatne, Kappel, S. L. (2020), "Real-Time Hand Gesture Recognition Using Temporal Muscle Activation Maps of Multi-Channel Semg Signals," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, Р. 1299-1303, DOI: 10.1109/ICASSP40776.2020.9054227
Asif, A.R.; Waris, A.; Gilani, S.O.; Jamil, M.; Ashraf, H.; Shafique, M.; Niazi, I.K. (2020), "Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG". Sensors. № 20, 1642 р. DOI: https://doi.org/10.3390/s20061642
Gopal, P.; Gesta, A.; Mohebbi, A. (2022), "A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models". Sensors. № 22, 3650 р. DOI: https://doi.org/10.3390/s22103650
Yu, G.; Deng, Z.; Bao, Z.; Zhang, Y.; He, B. (2023), "Gesture Classification in Electromyography Signals for Real-Time Prosthetic Hand Control Using a Convolutional Neural Network-Enhanced Channel Attention Model". Bioengineering. № 10, 1324 р. https://doi.org/10.3390/bioengineering10111324
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