A Review on the Recent use of Machine Learning for Gesture Recognition using Myoelectric Signals
Resumo
Gesture recognition using myoelectric signals (sEMG) is a powerful tool for Human-Machine Interfaces (HMIs). While significant progress has been made with various machine learning algorithms, more recent and robust solutions in the sEMG pipeline must be explored. This study reviews recent gesture recognition research to identify gaps and analyze standard classification and feature extraction approaches from sEMG signals. We performed a review considering studies published between 2018 and 2024. Our findings reveal a prevalence of public datasets and time-domain features. We highlight the need for further research on feature engineering, algorithm exploration beyond traditional choices, and integration of DL for feature extraction.
Palavras-chave:
Electromyography (EMG), Machine Learning, Feature Extraction
Referências
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Côté-Allard, U., Fall, C. L., Drouin, A., Campeau-Lecours, A., Gosselin, C., Glette, K., Laviolette, F., and Gosselin, B. (2019). Deep learning for electromyographic hand gesture signal classification using transfer learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4):760–771.
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Fajardo, J. M., Gomez, O., and Prieto, F. (2021). Emg hand gesture classification using handcrafted and deep features. Biomedical Signal Processing and Control, 63:102210.
Ghaffar Nia, N., Kaplanoglu, E., and Nasab, A. (2023). Emg-based hand gestures classification using machine learning algorithms. In SoutheastCon 2023, pages 787–792.
Huang, D. and Chen, B. (2019). Surface emg decoding for hand gestures based on spectrogram and cnn-lstm. In 2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI), pages 123–126.
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Javaid, H. A., Tiwana, M. I., Alsanad, A., Iqbal, J., Riaz, M. T., Ahmad, S., and Almisned, F. A. (2021). Classification of hand movements using myo armband on an embedded platform. Electronics, 10(11).
Jia, G., Lam, H.-K., Liao, J., and Wang, R. (2020). Classification of electromyographic hand gesture signals using machine learning techniques. Neurocomputing, 401:236–248.
Khan, M. U., Khan, H., Muneeb, M., Abbasi, Z., Abbasi, U. B., and Baloch, N. K. (2021). Supervised machine learning based fast hand gesture recognition and classification using electromyography (emg) signals. In 2021 International Conference on Applied and Engineering Mathematics (ICAEM), pages 81–86.
Krishnan, S. and Athavale, Y. (2018). Trends in biomedical signal feature extraction. Biomedical Signal Processing and Control, 43:41–63.
Mendes Junior, J. J. A., Freitas, M. L., Siqueira, H. V., Lazzaretti, A. E., Pichorim, S. F., and Stevan, S. L. (2020). Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by semg in eight channels armband approach. Biomedical Signal Processing and Control, 59:101920.
Oh, D. C. and Jo, Y. U. (2019). Emg-based hand gesture classification by scale average wavelet transform and cnn. In 2019 19th International Conference on Control, Automation and Systems (ICCAS), pages 533–538.
Ozdemir, M. A., Kisa, D. H., Guren, O., and Akan, A. (2022). Hand gesture classification using time–frequency images and transfer learning based on cnn. Biomedical Signal Processing and Control, 77:103787.
Ozdemir, M. A., Kisa, D. H., Guren, O., Onan, A., and Akan, A. (2020). Emg based hand gesture recognition using deep learning. In 2020 Medical Technologies Congress (TIPTEKNO), pages 1–4.
Phinyomark, A., N. Khushaba, R., and Scheme, E. (2018). Feature extraction and selection for myoelectric control based on wearable emg sensors. Sensors, 18(5).
Phinyomark, A., Phukpattaranont, P., and Limsakul, C. (2012). Feature reduction and selection for emg signal classification. Expert Systems with Applications, 39(8):7420–7431.
Samadani, A. (2018). Gated recurrent neural networks for emg-based hand gesture classification. a comparative study. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 1–4.
Sayin, F. S., Ozen, S., and Baspinar, U. (2018). Hand gesture recognition by using semg signals for human machine interaction applications. In 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pages 27–30.
Simão, M., Neto, P., and Gibaru, O. (2019). Emg-based online classification of gestures with recurrent neural networks. Pattern Recognition Letters, 128:45–51.
Somvanshi, M., Chavan, P., Tambade, S., and Shinde, S. V. (2016). A review of machine learning techniques using decision tree and support vector machine. In 2016 International Conference on Computing Communication Control and automation (IC-CUBEA), pages 1–7.
Sun, W., Liu, H., Tang, R., Lang, Y., He, J., and Huang, Q. (2019). semg-based hand-gesture classification using a generative flow model. Sensors, 19:1952.
Tavakoli, M., Benussi, C., Alhais Lopes, P., Osorio, L. B., and de Almeida, A. T. (2018). Robust hand gesture recognition with a double channel surface emg wearable armband and svm classifier. Biomedical Signal Processing and Control, 46:121–130.
Tepe, C. and Demir, M. (2022). Real-time classification of emg myo armband data using support vector machine. IRBM, 43(4):300–308.
Triwiyanto, T., Pawana, I. P. A., and Caesarendra, W. (2024). Deep learning approach to improve the recognition of hand gesture with multi force variation using electromyography signal from amputees. Medical Engineering & Physics, 125:104131.
Vásconez, J. P., Barona López, L. I., Ángel Leonardo Valdivieso Caraguay, and Benalcázar, M. E. (2023). A comparison of emg-based hand gesture recognition systems based on supervised and reinforcement learning. Engineering Applications of Artificial Intelligence, 123:106327.
Yoo, H.-J., Park, H.-j., and Lee, B. (2019). Myoelectric signal classification of targeted muscles using dictionary learning. Sensors, 19(10).
Zhang, R., Zhang, X., He, D., Wang, R., and Guo, Y. (2022). semg signals characterization and identification of hand movements by machine learning considering sex differences. Applied Sciences, 12(6).
Bi, L., Feleke, A. G., and Guan, C. (2019). A review on emg-based motor intention prediction of continuous human upper limb motion for human-robot collaboration. Biomedical Signal Processing and Control, 51:113–127.
Challa, K., AlHmoud, I. W., Kamrul Islam, A. K. M., and Gokaraju, B. (2023). Emg-based hand gesture recognition using individual sensors on different muscle groups. In 2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1–4.
Chen, L., Fu, J., Wu, Y., Li, H., and Zheng, B. (2020). Hand gesture recognition using compact cnn via surface electromyography signals. Sensors, 20(3).
Côté-Allard, U., Fall, C. L., Drouin, A., Campeau-Lecours, A., Gosselin, C., Glette, K., Laviolette, F., and Gosselin, B. (2019). Deep learning for electromyographic hand gesture signal classification using transfer learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4):760–771.
Esaa, R. R., Jaber, H. A., and Ameer, A. A. (2022). Hand movements classification based on myo armband signals. In 2022 4th International Conference on Electrical, Control and Instrumentation Engineering (ICECIE), pages 1–5.
Fajardo, J. M., Gomez, O., and Prieto, F. (2021). Emg hand gesture classification using handcrafted and deep features. Biomedical Signal Processing and Control, 63:102210.
Ghaffar Nia, N., Kaplanoglu, E., and Nasab, A. (2023). Emg-based hand gestures classification using machine learning algorithms. In SoutheastCon 2023, pages 787–792.
Huang, D. and Chen, B. (2019). Surface emg decoding for hand gestures based on spectrogram and cnn-lstm. In 2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI), pages 123–126.
Janiesch, C., Zschech, P., and Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31:685–695.
Javaid, H. A., Tiwana, M. I., Alsanad, A., Iqbal, J., Riaz, M. T., Ahmad, S., and Almisned, F. A. (2021). Classification of hand movements using myo armband on an embedded platform. Electronics, 10(11).
Jia, G., Lam, H.-K., Liao, J., and Wang, R. (2020). Classification of electromyographic hand gesture signals using machine learning techniques. Neurocomputing, 401:236–248.
Khan, M. U., Khan, H., Muneeb, M., Abbasi, Z., Abbasi, U. B., and Baloch, N. K. (2021). Supervised machine learning based fast hand gesture recognition and classification using electromyography (emg) signals. In 2021 International Conference on Applied and Engineering Mathematics (ICAEM), pages 81–86.
Krishnan, S. and Athavale, Y. (2018). Trends in biomedical signal feature extraction. Biomedical Signal Processing and Control, 43:41–63.
Mendes Junior, J. J. A., Freitas, M. L., Siqueira, H. V., Lazzaretti, A. E., Pichorim, S. F., and Stevan, S. L. (2020). Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by semg in eight channels armband approach. Biomedical Signal Processing and Control, 59:101920.
Oh, D. C. and Jo, Y. U. (2019). Emg-based hand gesture classification by scale average wavelet transform and cnn. In 2019 19th International Conference on Control, Automation and Systems (ICCAS), pages 533–538.
Ozdemir, M. A., Kisa, D. H., Guren, O., and Akan, A. (2022). Hand gesture classification using time–frequency images and transfer learning based on cnn. Biomedical Signal Processing and Control, 77:103787.
Ozdemir, M. A., Kisa, D. H., Guren, O., Onan, A., and Akan, A. (2020). Emg based hand gesture recognition using deep learning. In 2020 Medical Technologies Congress (TIPTEKNO), pages 1–4.
Phinyomark, A., N. Khushaba, R., and Scheme, E. (2018). Feature extraction and selection for myoelectric control based on wearable emg sensors. Sensors, 18(5).
Phinyomark, A., Phukpattaranont, P., and Limsakul, C. (2012). Feature reduction and selection for emg signal classification. Expert Systems with Applications, 39(8):7420–7431.
Samadani, A. (2018). Gated recurrent neural networks for emg-based hand gesture classification. a comparative study. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 1–4.
Sayin, F. S., Ozen, S., and Baspinar, U. (2018). Hand gesture recognition by using semg signals for human machine interaction applications. In 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pages 27–30.
Simão, M., Neto, P., and Gibaru, O. (2019). Emg-based online classification of gestures with recurrent neural networks. Pattern Recognition Letters, 128:45–51.
Somvanshi, M., Chavan, P., Tambade, S., and Shinde, S. V. (2016). A review of machine learning techniques using decision tree and support vector machine. In 2016 International Conference on Computing Communication Control and automation (IC-CUBEA), pages 1–7.
Sun, W., Liu, H., Tang, R., Lang, Y., He, J., and Huang, Q. (2019). semg-based hand-gesture classification using a generative flow model. Sensors, 19:1952.
Tavakoli, M., Benussi, C., Alhais Lopes, P., Osorio, L. B., and de Almeida, A. T. (2018). Robust hand gesture recognition with a double channel surface emg wearable armband and svm classifier. Biomedical Signal Processing and Control, 46:121–130.
Tepe, C. and Demir, M. (2022). Real-time classification of emg myo armband data using support vector machine. IRBM, 43(4):300–308.
Triwiyanto, T., Pawana, I. P. A., and Caesarendra, W. (2024). Deep learning approach to improve the recognition of hand gesture with multi force variation using electromyography signal from amputees. Medical Engineering & Physics, 125:104131.
Vásconez, J. P., Barona López, L. I., Ángel Leonardo Valdivieso Caraguay, and Benalcázar, M. E. (2023). A comparison of emg-based hand gesture recognition systems based on supervised and reinforcement learning. Engineering Applications of Artificial Intelligence, 123:106327.
Yoo, H.-J., Park, H.-j., and Lee, B. (2019). Myoelectric signal classification of targeted muscles using dictionary learning. Sensors, 19(10).
Zhang, R., Zhang, X., He, D., Wang, R., and Guo, Y. (2022). semg signals characterization and identification of hand movements by machine learning considering sex differences. Applied Sciences, 12(6).
Publicado
17/11/2024
Como Citar
DE LIMA, Gabriel Molina; CAMPOS, Daniel Prado; MANTOVANI, Rafael Gomes.
A Review on the Recent use of Machine Learning for Gesture Recognition using Myoelectric Signals. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 180-191.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245071.