Muscle-Controlled vs. Neuro-Controlled Prosthetic Hands: Technologies, Challenges, and Future Directions
DOI:
https://doi.org/10.58445/rars.4314Keywords:
Muscle-Controlled Prosthetic Hands, Neuro-Controlled Prosthetic Hands, Bionic prostheticsAbstract
Along with the significant advancement of bionic prosthetics, new opportunities emerged to restore hand function and independence of individuals that suffer limb loss. However, replicating the dexterity, coordination, and accurate sensory feedback of a natural hand remains challenging. This paper analyzes and compares two major approaches to prosthetic hand control: muscle-controlled and neuro-controlled systems. This article examines their control mechanisms, current level of technology, advantages, and limitations, focusing on signal acquisition, movement control, invasiveness, and sensory feedback. Muscle controlled prostheses interpret electrical activity generated by residual muscles to initiate intended movements, offering a practical approach of prosthetic control without direct neural interfaces. Neuro-controlled prostheses, in contrast, collect signals directly from the human brain or peripheral nervous system to control prostheses movement. Moreover, some neural systems provide sensory feedback. Although muscle controlled systems are generally more accessible, their performance can be affected and limited by multiple factors. Neural-controlled systems offer a higher potential for a higher freedom of movement and sensory feedback, it faces challenges related to surgical risks, high maintenance cost, and long term stability. By comparing these two approaches, this paper evaluates their respective strengths and weaknesses and discusses future directions for developing enhanced bionic hands.
References
Aman, M., Sporer, M. E., Gstoettner, C., Prahm, C., Hofer, C., Mayr, W., ... & Aszmann, O. C. (2019). Bionic hand as artificial organ: Current status and future perspectives. Artificial organs, 43(2), 109-118.
Grushko, S., Spurný, T., & Černý, M. (2020). Control methods for transradial prostheses based on remnant muscle activity and its relationship with proprioceptive feedback. Sensors, 20(17), 4883.
(2019, July 24). Prosthetic limbs with neural connections – discovery and innovation at University of Utah Health. Utah.Edu. https://discovery.med.utah.edu/2019/prosthetic-limbs-with-neural-connections/
Schwartz, A. B., Cui, X. T., Weber, D. J., & Moran, D. W. (2006). Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron, 52(1), 205-220.
Uellendahl, J. (2021). Body-powered prosthetic systems. In Bionic limb reconstruction (pp. 27-35). Cham: Springer International Publishing.
Geethanjali, P. (2016). Myoelectric control of prosthetic hands: state-of-the-art review. Medical Devices: Evidence and Research, 247-255.
Schultz, A. E., & Kuiken, T. A. (2011). Neural interfaces for control of upper limb prostheses: the state of the art and future possibilities. Pm&r, 3(1), 55-67.
Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., ... & Shenoy, K. V. (2012). A high-performance neural prosthesis enabled by control algorithm design. Nature neuroscience, 15(12), 1752-1757.
Moradi, A., Rafiei, H., Daliri, M., Akbarzadeh-T, M. R., Akbarzadeh, A., Naddaf-Sh, A. M., & Naddaf-Sh, S. (2022). Clinical implementation of a bionic hand controlled with kineticomyographic signals. Scientific Reports, 12(1), 14805.
Reaz, M. B. I., Hussain, M. S., & Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: detection, processing, classification and applications. Biological procedures online, 8(1), 11-35.
Fu, J., Choudhury, R., Hosseini, S. M., Simpson, R., & Park, J. H. (2022). Myoelectric control systems for upper limb wearable robotic exoskeletons and exosuits—A systematic review. Sensors, 22(21), 8134.
Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., ... & Shenoy, K. V. (2012). A high-performance neural prosthesis enabled by control algorithm design. Nature neuroscience, 15(12), 1752-1757.
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., ... & Donoghue, J. P. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164-171.
Disselhorst-Klug, C., Schmitz-Rode, T., & Rau, G. (2009). Surface electromyography and muscle force: Limits in sEMG–force relationship and new approaches for applications. Clinical biomechanics, 24(3), 225-235.
Kyranou, I., Vijayakumar, S., & Erden, M. S. (2018). Causes of performance degradation in non-invasive electromyographic pattern recognition in upper limb prostheses. Frontiers in neurorobotics, 12, 58.
Taylor, T. D. (1998). Prosthodontic problems and limitations associated with osseointegration. The Journal of prosthetic dentistry, 79(1), 74-78.
Raspopovic, S., Capogrosso, M., Petrini, F. M., Bonizzato, M., Rigosa, J., Di Pino, G., ... & Micera, S. (2014). Restoring natural sensory feedback in real-time bidirectional hand prostheses. Science translational medicine, 6(222), 222ra19-222ra19.
Shen, K., Chen, O., Edmunds, J. L., Piech, D. K., & Maharbiz, M. M. (2023). Translational opportunities and challenges of invasive electrodes for neural interfaces. Nature Biomedical Engineering, 7(4), 424-442.
Downloads
Posted
License
Copyright (c) 2026 Research Archive of Rising Scholars
This work is licensed under a Creative Commons Attribution 4.0 International License.
