Comparative Study on Different Control Methods of Limb Prostheses

Keyan Shen; Zixin Zhang; Shuoleilei Guo

doi:10.54097/hrc8hz14

Authors

Keyan Shen
Zixin Zhang
Shuoleilei Guo

DOI:

https://doi.org/10.54097/hrc8hz14

Keywords:

Prosthetic Control, EMG, Neuromusculoskeletal Integration, Vision-Based Control, EEG-EMG Interface

Abstract

This paper presents a comparative analysis of three advanced prosthetic control methods: EMG-based systems, neuromusculoskeletal integration, and hybrid-sensing approaches (including vision-based and EEG-EMG systems). EMG control, while well-established and non-invasive, demonstrates limitations in signal accuracy and muscle dependency. Neuromusculoskeletal systems provide more intuitive movement control through osseointegration, though they currently lack sophisticated sensory feedback capabilities. Hybrid-sensing methods represent the cutting edge, with vision-based systems enabling environmental interaction through object recognition and EEG-EMG systems offering more natural control by combining neural and muscular signals. While promising, these hybrid approaches present implementation challenges, including computational complexity and user adaptation requirements. The study evaluates the clinical applicability, technological maturity, and future development potential of each method, offering insights for optimizing prosthetic design to better meet user needs.

Downloads

Download data is not yet available.

References

[1] Cheng, L., Li, D., Yu, G., Zhang, Z., & Yu, S. (2022). Robotic arm control system based on brain-muscle mixed signals. Biomedical Signal Processing and Control, 77, 103754. https://doi.org/10. 1016/j. bspc. 2022. 103754.

[2] Chowdhury, A., Raza, H., Yogesh Kumar Meena, Dutta, A., & Prasad, G. (2019). An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation. Journal of Neuroscience Methods, 312, 1–11. https://doi.org/10.1016/j.jneumeth.2018.11.010.

[3] del Olmo, M., & Domingo, R. (2020). EMG Characterization and Processing in Production Engineering. Materials, 13(24), 5815. https://doi.org/10.3390/ma13245815.

[4] Goodale, M. A. (2014). How (and why) the visual control of action differs from visual perception. Proceedings of the Royal Society B: Biological Sciences, 281(1785), 20140337–20140337. https:// doi. org/ 10.1098/rspb.2014.0337.

[5] Graczyk, E. L., Resnik, L., Schiefer, M. A., Schmitt, M. S., & Tyler, D. J. (2018). Home Use of a Neural-connected Sensory Prosthesis Provides the Functional and Psychosocial Experience of Having a Hand Again. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-26952-x.

[6] Gu, G., Zhang, N., Xu, H., Lin, S., Yu, Y., Chai, G., Ge, L., Yang, H., Shao, Q., Sheng, X., Zhu, X., & Zhao, X. (2021). A soft neuroprosthetic hand providing simultaneous myoelectric control and tactile feedback. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-021-00767-0.

[7] He, Y., Ryusuke Kubozono, Fukuda, O., Yamaguchi, N., & Okumura, H. (2020). Vision-Based Assistance for Myoelectric Hand Control. IEEE Access, 8, 201956–201965. https://doi.org/10. 1109/ access. 2020.3036115.

[8] Jacob, S., Menon, V. G., & Joseph, S. (2019). Artificial Intelligence Powered EEG-EMG Electrodes for Assisting the Paralyzed. IEEE Technology Policy and Ethics, 4(4), 1–5. https://doi.org/10.1109/ ntpe. 2019.9778119.

[9] Middleton, A., & Ortiz-Catalan, M. (2020). Neuromusculoskeletal Arm Prostheses: Personal and Social Implications of Living with an Intimately Integrated Bionic Arm. Frontiers in Neurorobotics, 14, 39. https://doi.org/10.3389/fnbot.2020.00039.

[10] Petrini, F. M., Valle, G., Strauss, I., Granata, G., Di Iorio, R., D’Anna, E., Čvančara, P., Mueller, M., Carpaneto, J., Clemente, F., Controzzi, M., Bisoni, L., Carboni, C., Barbaro, M., Iodice, F., Andreu, D., Hiairrassary, A., Divoux, J.-L., Cipriani, C., & Guiraud, D. (2018). Six-Month Assessment of a Hand Prosthesis with Intraneural Tactile Feedback. Annals of Neurology, 85(1), 137–154. https://doi. org/ 10. 1002/ ana.25384.

[11] Sun, Y., Cheng, N., Zhang, S., Li, W., Yang, L., Cui, S., Liu, H., Sun, F., Zhang, J., Guo, D., Han, W., & Fang, B. (2025). Tactile data generation and applications based on visuo-tactile sensors: A review. Information Fusion, 121, 103162. https://doi.org/10.1016/j.inffus.2025.103162.