Overview of Machine Learning Methods in Prosthetics
DOI:
https://doi.org/10.52575/2687-0932-2025-52-4-897-927Keywords:
machine learning in prosthetics, electromyographic signals, neural networks, prosthetic control, EMG signal processingAbstract
The purpose of the research is to analyze modern machine learning methods for processing electromyographic (EMG) signals used in the control of advanced prosthetics. The study aims to compare the effectiveness of classical and neural network approaches, evaluate their accuracy, and identify key factors influencing the results. The article provides a review of existing research dedicated to the processing of EMG signals using machine learning. Popular datasets (e.g., NinaPro) as well as various signal processing methods were reviewed: classical ones (LDA, KNN) and modern neural network architectures (EMGHandNet, CNN-RNN, etc.). Special attention is given to the comparative analysis of model accuracy depending on the used data, architectures, and method parameters. The analysis showed that modern neural network models (ConTraNet, CNN-RNN) demonstrate higher accuracy compared to classical methods (SVM, LDA, RF, etc.), however, their effectiveness heavily depends on the quality and diversity of the data. Limitations have been identified related to insufficient testing on various datasets, indicating the need for standardization of experiments. The importance of signal preprocessing and the quality of EMG sensors for achieving stable results has also been confirmed. The application of machine learning methods, especially neural network architectures, is promising for creating more accurate and adaptive prosthetics. However, further development of the technology requires addressing the issues of model generalization, expanding test data, and improving their quality. Additional research should focus on integrating systems into real-world operating conditions and improving the interpretability of results.
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Englehart K., Hudgins B. 2003. A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Bio-Medical Engineering, 50(7): 848–854.
Englehart K., Hudgins B., Parker P.A., Stevenson M. 1999. Classification of the myoelectric signal using time-frequency based representations. Med. Eng. Phys., 21: 431–438
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Geng Ya., Zhang X., Zhang Y.T., Li P. 2014. A novel channel selection method for multiple motion classification using high-density electromyography. Biomedical engineering online, 13: 102. 10.1186/1475-925X-13-102.
Goldberger A.L., et al. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23): 215–220.
Graupe D., Cline W.K. 1975. Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes. IEEE Trans Syst Man Cybern, SMC-5: 252–259.
He K., Zhang X., Ren S., Sun J. 2016. Deep residual learning for image recognition. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, 770–778.
Hu Yu., Wong Yo., Wei W., Du Yu., Kankanhalli M., Geng W. 2018. A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition. PLOS One, 13. e0206049. 10.1371/journal.pone.0206049.
Hudgins B., Parker P., Scott R.N. 1993. A New Strategy for Multifunction Myoelectric Control. IEEE Trans. Biomed. Eng., 40(1): 82– 94.
Jia G., Lam H.-K., Liao Ju., Wang R. 2020. Classification of Electromyographic Hand Gesture Signals using Machine Learning Techniques. Neurocomputing, 401. 10.1016/j.neucom.2020.03.009.
Karnam N.K., Dubey Sh.R., Turlapaty A., Gokaraju B. 2022. EMGHandNet: A hybrid CNN and Bi-LSTM architecture for hand activity classification using surface EMG signals. Biocybernetics and Biomedical Engineering, 42. 10.1016/j.bbe.2022.02.005.
Khushaba R.N., Kodagoda S., Takruri M., Dissanayake G. 2012. Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Syst. Appl., 39(12): 10731–10738, Sep.
Kilic E. 2017. EMG based neural network and admittance control of an active wrist orthosis, J. Mech. Sci. Technol., 31(12): 6093–6106,
Kimoto A., Fujiyama H., Machida M. 2023. A Wireless Multi-Layered EMG/MMG/NIRS Sensor for Muscular Activity Evaluation. Sensors, 23. 1539. 10.3390/s23031539.
Kuiken T.A., Li G., Lock B.A. et al. 2009. Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA, 2009 Feb 11. 301(6): 619–628.
Lawhern V.J., Solon A.J., Waytowich N.R., Gordon S.M., Hung C.P., Lance B.J. 2018. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces, J. Neural. Eng, (Oct. 2018), 15(5). 056013.
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Ng C.L., Reaz M.B.I., Crespo M., Cicuttin A., Shapiai M., Ali S. 2024. A Versatile and Wireless Multichannel Capacitive EMG Measurement System for Digital Healthcare. IEEE Internet of Things Journal, 1-1. 10.1109/JIOT.2024.3370960.
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Ortiz-Catalan M., Branemark R., Hakansson B. 2013. BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms. Source Code Biol. Med., 8(1): 11.
Ozdemir M.A. 2021. Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures, Mendeley, 15.
Ozdemir M.A., Kisa D.H., Guren O., Akan A. 2022. Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures, Data Brief, 41. 107921.
Pizzolato S., Tagliapietra L., Cognolato M., Reggiani M., Muller H., Atzori M. 2017. Comparison of six electromyography acquisition setups on hand movement classification tasks. PLOS One, 10; 12(10): 1–17.
Resnik L. 2011. Development and testing of new upper-limb prosthetic devices: research designs for usability testing. J. Rehabil. Res. Dev, 48(6): 697–706.
Ryser F., Butzer T., Held J.P., Lambercy O., Gassert R. 2017. Fully embedded myoelectric control for a wearable robotic hand orthosis. IEEE Int. Conf. Rehabil. Robot. 2017 Jul. 615–621. doi: 10.1109/ICORR.2017.8009316. PMID: 28813888.
Sapsanis C., Georgoulas G., Tzes A., Lymberopoulos D. 2013. Improving EMG based classification of basic hand movements using EMD.
Schirrmeister R.T., et al. 2017. Deep learning with convolutional neural networks for EEG decoding and visualization: convolutional Neural Networks in EEG Analysis, Hum. Brain Mapp, 38(11): 5391–5420.
Song W., Wang A., Chen Ya., Bai Sh., Lin Zh., Yan N., Luo D., Liao Yi., Zhang M., Wang Zh., Xie X. 2019. Design of a Wearable Smart sEMG Recorder Integrated Gradient Boosting Decision Tree based Hand Gesture Recognition. IEEE transactions on biomedical circuits and systems, 10.1109/TBCAS.2019.2953998.
Triwiyanto T., Wahyunggoro O., Nugroho H.A., Herianto H. 2017. An investigation into time domain features of surface electromyography to estimate the elbow joint angle. Adv. Electr. Electron. Eng., 15(3): 448–458,
Triwiyanto T., Pawana I., Purnomo M. 2020. An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 1-1. 10.1109/TNSRE.2020.2999505.
Valdivieso L., Vásconez H.Ju., Barona L., Benalcázar M. 2023. Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks. Sensors, 23. 10.3390/s23083905.
Vásconez J.P., Barona L.L.I., Valdivieso C.A.L., Benalcázar M.E. 2022. Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks. Sensors, 22, 9613.
Wei W., Wong Y., Du Y., Hu Y., Kankanhalli M., Geng W. 2019. A multi-stream convolutional neural network for sEMG-based gesture recognition in musclecomputer interface. Pattern Recognit. Lett., 119: 131–138.
Young A.J., Hargrove L.J., Kuiken T.A. 2011. The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift. IEEE Trans. Biomed. Eng., 58(9): 2537–2544
Zhang Ch., Shih Ya.-H., Qian Ji. 2019. Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network. Sensors, 19. 3170. 10.3390/s19143170.
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Amma C., Krings T., Bo¨er J., Schultz T. 2015. Advancing Muscle-Computer Interfaces with High-Density Electromyography. In: ACM Conference on Human Factors in Computing Systems. 929–938.
Arteaga M., Castiblanco J., Mondragón I., Colorado Ju., Alvarado-Rojas C. 2020. EMG-driven hand model based on the classification of individual finger movements. Biomedical Signal Processing and Control, 58. 101834. 10.1016/j.bspc.2019.101834.
Ashford J., Bird J.J., Campelo F., Faria D.R. 2019. Classification of EEG signals based on image representation of statistical features. Proc. UK Workshop Comput. Intell. Portsmouth, U.K.: Springer,
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Atzori M., Gijsberts A., Castellini C., Caputo B., Hager A.G.M., Elsig S., et al. 2014. Electromyography data for noninvasive naturally-controlled robotic hand prostheses. Scientific data, 1.
Bakırcıoğlu K., Ozkurt N. 2020. Classification of Emg Signals Using Convolution Neural Network. International Journal of Applied Mathematics Electronics and Computers, 8. 10.18100/ijamec.795227.
Becerra-Fajardo L., Minguillon Je., Krob M., Rodrigues de C.C., Gonzalez-Sanchez M., Megía-García Á., Galán C., Henares F., Comerma A., del-Ama A., Gil-Agudo A., Grandas F., Schneider-Ickert A., Barroso F., Ivorra A. 2024. First-in-human demonstration of floating EMG sensors and stimulators wirelessly powered and operated by volume conduction. Journal of NeuroEngineering and Rehabilitation, 21. 10.1186/s12984-023-01295-5.
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Bird J., Kobylarz Jh., Faria D, Ekárt A., Ribeiro E. 2020. Cross-domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG. IEEE Access, 1-1. 10.1109/ACCESS.2020.2979074.
Bird J.J., Faria D.R., Manso L.J., Ekárt A., Buckingham C.D. 2019. A deep evolutionary approach to bioinspired classifier optimisation for brain-machine interaction. Complexity, vol. 2019, 1–14, Mar.
Chen T., Guestrin C. 2016. Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 785–794.
Chen X., Li Y., Hu R., Zhang X., Chen X. 2021. Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method. IEEE J Biomed Health Inform, 25(4): 1292–1304.
Dewald H.A., Lukyanenko P., Lambrecht J.M., Anderson J.R., Tyler D.J., Kirsch R.F., et al. 2019. Stable, three degree-of-freedom myoelectric prosthetic control via chronic bipolar intramuscular electrodes: a case study. J Neuroeng Rehabil, 16(1): 147.
Di Nardo F. 2019. Intra-subject classification of gait phases by neural network interpretation of EMG signals.
Englehart K., Hudgins B. 2003. A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Bio-Medical Engineering. 50(7): 848–854.
Englehart K., Hudgins B., Parker P.A., Stevenson M. 1999. Classification of the myoelectric signal using time-frequency based representations. Med. Eng. Phys., 21: 431–438
Friedman J.H. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189–1232.
Geng W., Du Y., Jin W., Wei W., Hu Y., Li J. 2016. Gesture recognition by instantaneous surface EMG images. Scientific Reports, 6: 36571.
Geng Ya., Zhang X., Zhang Y.T., Li P. 2014. A novel channel selection method for multiple motion classification using high-density electromyography. Biomedical engineering online, 13: 102. 10.1186/1475-925X-13-102.
Goldberger A.L., et al. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23): 215–220.
Graupe D., Cline W.K. 1975. Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes. IEEE Trans Syst Man Cybern, SMC-5: 252–259.
He K., Zhang X., Ren S., Sun J. 2016. Deep residual learning for image recognition. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, 770–778.
Hu Yu., Wong Yo., Wei W., Du Yu., Kankanhalli M., Geng W. 2018. A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition. PLOS One, 13. e0206049. 10.1371/journal.pone.0206049.
Hudgins B., Parker P., Scott R.N. 1993.A New Strategy for Multifunction Myoelectric Control. IEEE Trans. Biomed. Eng., 40(1): 82– 94.
Jia G., Lam H.-K., Liao Ju., Wang R. 2020. Classification of Electromyographic Hand Gesture Signals using Machine Learning Techniques. Neurocomputing, 401. 10.1016/j.neucom.2020.03.009.
Karnam N.K., Dubey Sh.R., Turlapaty A., Gokaraju B. 2022. EMGHandNet: A hybrid CNN and Bi-LSTM architecture for hand activity classification using surface EMG signals. Biocybernetics and Biomedical Engineering, 42. 10.1016/j.bbe.2022.02.005.
Khushaba R.N., Kodagoda S., Takruri M., Dissanayake G. 2012. Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Syst. Appl., 39(12): 10731–10738, Sep.
Kilic E. 2017. EMG based neural network and admittance control of an active wrist orthosis, J. Mech. Sci. Technol., 31(12): 6093–6106,
Kimoto A., Fujiyama H., Machida M. 2023. A Wireless Multi-Layered EMG/MMG/NIRS Sensor for Muscular Activity Evaluation. Sensors, 23. 1539. 10.3390/s23031539.
Kuiken T.A., Li G., Lock B.A. et al. 2009. Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA. 2009 Feb 11. 301(6): 619–628.
Lawhern V.J., Solon A.J., Waytowich N.R., Gordon S.M., Hung C.P., Lance B.J. 2018. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces, J. Neural. Eng, (Oct. 2018), 15(5). 056013.
Lee K.H., Min Ji., Byun S. 2021. Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks. Sensors, 22: 225. 10.3390/s22010225.
Lerner Z.F., Board W.J., Browning R.C. 2014. Effects of obesity on lower extremity muscle function during walking at two speeds. Gait Posture, 39(3): 978–984.
Lobov S., Krilova N., Kastalskiy I., Kazantsev V., Makarov V.A. 2018. Latent factors limiting the performance of sEMG-interfaces. Sensors, 18(4): 1122.
Motoche C., Benalcázar M.E. 2018. Real-time hand gesture recognition based on electromyographic signals and artificial neural networks. In Proceedings of the International Conference on Artificial Neural Networks, Rhodes, Greece, 4–7 October 2018; 352–361.
Nazarpour K., Sharafat A.R., Firoozabadi S.M.P. 2007. Application of higher order statistics to surface electromyogram signal classification. IEEE Trans. Biomed. Eng., 54:1762–1769.
Ng C.L., Reaz M.B.I., Crespo M., Cicuttin A., Shapiai M., Ali S. 2024. A Versatile and Wireless Multichannel Capacitive EMG Measurement System for Digital Healthcare. IEEE Internet of Things Journal, 1-1. 10.1109/JIOT.2024.3370960.
Ng Ch.L., Reaz M.B.I., Crespo M., Cicuttin A., Shapiai M., Ali S., Kamal N. 2023. A Flexible Capacitive Electromyography Biomedical Sensor for Wearable Healthcare Applications. IEEE Transactions on Instrumentation and Measurement, 1-1. 10.1109/TIM.2023.3281563.
Olsson A.E., Bjorkman A., Antfolk C. 2020. Automatic discovery of resource-restricted convolutional neural network topologies for myoelectric pattern recognition. Comput. Biol. Med., 120: 103723.
Ortiz-Catalan M., Branemark R., Hakansson B. 2013. BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms. Source Code Biol. Med., 8(1): 11.
Ozdemir M.A. 2021. Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures, Mendeley, 15.
Ozdemir M.A., Kisa D.H., Guren O., Akan A. 2022. Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures, Data Brief, 41. 107921.
Pizzolato S., Tagliapietra L., Cognolato M., Reggiani M., Muller H., Atzori M. 2017. Comparison of six electromyography acquisition setups on hand movement classification tasks. PLOS One, 10; 12(10): 1–17.
Resnik L. 2011. Development and testing of new upper-limb prosthetic devices: research designs for usability testing. J. Rehabil. Res. Dev, 48(6): 697–706.
Ryser F., Butzer T., Held J.P., Lambercy O., Gassert R. 2017. Fully embedded myoelectric control for a wearable robotic hand orthosis. IEEE Int. Conf. Rehabil. Robot. 2017 Jul. 615–621. doi: 10.1109/ICORR.2017.8009316. PMID: 28813888.
Sapsanis C., Georgoulas G., Tzes A., Lymberopoulos D. 2013. Improving EMG based classification of basic hand movements using EMD.
Schirrmeister R.T., et al. 2017. Deep learning with convolutional neural networks for EEG decoding and visualization: convolutional Neural Networks in EEG Analysis, Hum. Brain Mapp., 38(11): 5391–5420.
Song W., Wang A., Chen Ya., Bai Sh., Lin Zh., Yan N., Luo D., Liao Yi., Zhang M., Wang Zh., Xie X. 2019. Design of a Wearable Smart sEMG Recorder Integrated Gradient Boosting Decision Tree based Hand Gesture Recognition. IEEE transactions on biomedical circuits and systems, 10.1109/TBCAS.2019.2953998.
Triwiyanto T., Wahyunggoro O., Nugroho H.A., Herianto H. 2017. An investigation into time domain features of surface electromyography to estimate the elbow joint angle. Adv. Electr. Electron. Eng., 15(3): 448–458,
Triwiyanto T., Pawana I., Purnomo M. 2020. An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 1-1. 10.1109/TNSRE.2020.2999505.
Valdivieso L., Vásconez H.Ju., Barona L., Benalcázar M. 2023. Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks. Sensors, 23. 10.3390/s23083905.
Vásconez J.P., Barona L.L.I., Valdivieso C.A.L., Benalcázar M.E. 2022. Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks. Sensors, 22, 9613.
Wei W., Wong Y., Du Y., Hu Y., Kankanhalli M., Geng W. 2019. A multi-stream convolutional neural network for sEMG-based gesture recognition in musclecomputer interface. Pattern Recognit. Lett., 119: 131–138.
Young A.J., Hargrove L.J., Kuiken T.A. 2011. The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift. IEEE Trans. Biomed. Eng., 58(9): 2537–2544
Zhang Ch., Shih Ya.-H., Qian Ji. 2019. Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network. Sensors, 19. 3170. 10.3390/s19143170.
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