Abstract
There are several factors that affect the sEMG signal during the process from its generation to its acquisition by sEMG devices. In this study, we tried to explain the physiological functional relationship between sEMG signals and muscles and between muscles and gestures in the human right forearm to increase confidence in the application of artificial intelligence in the medical field. For this purpose, we simulated the muscle and electrode positions with a 3D model to calculate their distance relationship, designed a cuff based on this model, and considered the effect of different distance solving methods on gesture recognition. The results showed that the highest accuracy of 93.95% was achieved for gesture recognition with the center of gravity method to find the electrode-to-muscle distance when the ratio of muscle electrode distance to the number of nerve muscle branches was 1:0.1. It is explained that the distance factor is the main factor affecting the recognition of sEMG signals, and an appropriate increase in the muscle length or neuromuscular branch number factor will play a positive role in the accuracy. The visualization of muscle activation further verifies and explains the relationship between sEMG signals and muscles, which makes the rehabilitation training more scientific and effective.
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References
Maksymenko, K., Clarke, A.K., Mendez Guerra, I., Deslauriers-Gauthier, S., Farina, D.: A myoelectric digital twin for fast and realistic modelling in deep learning. Nat. Commun. 14(1), 1600 (2023)
Zhao, J., Yu, Y., Sheng, X., Zhu, X.: Extracting stable control information from EMG signals to drive a musculoskeletal model - a preliminary study. In: Liu, H., et al (eds.). Intelligent Robotics and Applications. ICIRA 2022, vol. 13456. Lecture Notes in Computer Science. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-13822-5_66
Roy, J., Ali, M.A., Ahmed, M.R., Sundaraj, K.: Machine learning techniques for predicting surface EMG activities on upper limb muscle: a systematic review. In: Bhuiyan, T., Rahman, MdMostafijur, Ali, M.A. (eds.) ICONCS 2020. LNICSSITE, vol. 325, pp. 330–339. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52856-0_26
Fang, B., et al.: Simultaneous semg recognition of gestures and force levels for interaction with prosthetic hand. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2426–2436 (2022)
Zheng, J., Xie, C., Song, R., Xu, T.: A statistical learning approach for cable-driven robot in rehabilitation training. In: 2022 International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 296−301. IEEE (2022)
Zhang, J., Liu, Y., Liu, J.: Wearable Sensing based virtual reality rehabilitation scheme for upper limb training. In: Liu, H., et al. (eds.). Intelligent Robotics and Applications. ICIRA 2022, vol. 13457. Lecture Notes in Computer Science(), Springer, Cham (2022). https://doi.org/10.1007/978-3-031-13835-5_3
Mukhopadhyay, A.K., Samui, S.: An experimental study on upper limb position invariant emg signal classification based on deep neural network. Biomed. Signal Process. Control 55, 101669 (2020)
Yu, J., Gao, H., Zhou, D., Liu, J., Gao, Q., Ju, Z.: Deep temporal model-based identity-aware hand detection for space human–robot interaction. IEEE Trans. Cybern. 52(12), 13738–13751 (2021)
Zhong, T., Li, D., Wang, J., Xu, J., An, Z., Zhu, Y.: Fusion learning for semg recognition of multiple upper-limb rehabilitation movements. Sensors 21(16), 5385 (2021)
Vijayvargiya, A., Singh, P., Kumar, R., Dey, N.: Hardware implementation for lower limb surface emg measurement and analysis using explainable ai for activity recognition. IEEE Trans. Instrum. Meas. 71, 1–9 (2022)
Yu, J., Gao, H., Chen, Y., Zhou, D., Liu, J., Ju, Z.: Adaptive spatiotemporal representation learning for skeleton-based human action recognition. IEEE Trans. Cogn. Dev. Syst. 14(4), 1654–1665 (2021)
Yu, J., Xu, Y., Chen, H., Ju, Z.: Versatile graph neural networks toward intuitive human activity understanding. IEEE Trans. Neural Netw. Learn. Syst. (2022)
Amezquita-Garcia, J., Bravo-Zanoguera, M., Gonzalez-Navarro, F.F., Lopez-Avitia, R., Reyna, M.: Applying machine learning to finger movements using electromyography and visualization in opensim. Sensors 22(10), 3737 (2022)
Cheng, Y., et al.: Visualization of activated muscle area based on semg. J. Intell. Fuzzy Syst. 38(3), 2623–2634 (2020)
Acknowledgements
The authors would like to acknowledge the support from National Natural Science Foundation of China (grant No. 52075530) and the AiBle project co-financed by the European Regional Development Fund, and Zhejiang Provincial Natural Science Foundation of China (LQ23F030001).
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Shi, J., Liu, M., Fang, Y., Yu, J., Gao, H., Ju, Z. (2023). Examining the Impact of Muscle-Electrode Distance in sEMG Based Hand Motion Recognition. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14269. Springer, Singapore. https://doi.org/10.1007/978-981-99-6489-5_5
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