Artificial Intelligence-Integrated Dynamic Prostheses
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Artificial intelligence-integrated dynamic prostheses will represent a transformative leap in rehabilitative and assistive technologies, offering amputees and individuals with limb deficiencies unprecedented mobility, adaptability, and user-centered control. These advanced prosthetics combine cutting-edge artificial intelligence (AI), machine learning algorithms, neural interfaces, and biomechanical engineering to create responsive, intuitive devices that mimic the complexity of natural limb movement. Unlike traditional static prostheses, AI-enhanced models learn from users' motion patterns, adjust to environmental variables, and improve over time through continuous feedback loops. Their development involves multidisciplinary collaboration between data scientists, engineers, neuroscientists, and clinicians. While the field shows immense promise—improving functional outcomes, enhancing quality of life, and reducing physical and psychological burdens—challenges remain in accessibility, affordability, neural integration, and long-term user adaptability.
##plugins.themes.bootstrap3.article.details##
Artificial Intelligence, Dynamic Prostheses, Feedback Loop, Adaptability, Quality of Life
No funding source declared.
Cordella, F., Ciancio, A. L., Sacchetti, R., Davalli, A., Cutti, A. G., Guglielmelli, E., & Zollo, L. (2016). Literature Review on Needs of Upper Limb Prosthesis Users. Frontiers in Neuroscience, 10. https://doi.org/10.3389/fnins.2016.00209
Dababneh, S., Dababneh, N., Xie, C., Henchi, H., & Efanov, J. I. (2025). Shaping the future of upper extremity prostheses through 3D printing. Prosthesis, 7(2), 39. https://doi.org/10.3390/prosthesis7020039
Gupta, A., Vardalakis, N., & Wagner, F. B. (2023). Neuroprosthetics: from sensorimotor to cognitive disorders. Communications Biology, 6(1). https://doi.org/10.1038/s42003-022-04390-w
Hussain, S. a. H., Raza, I., Hussain, S. A., Jamal, M. H., Gulrez, T., & Zia, A. (2025). A mental state aware brain computer interface for adaptive control of electric powered wheelchair. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-024-82252-7
Kristoffersen, M. B., Franzke, A. W., Bongers, R. M., Wand, M., Murgia, A., & Van Der Sluis, C. K. (2021). User training for machine learning controlled upper limb prostheses: a serious game approach. Journal of NeuroEngineering and Rehabilitation, 18(1). https://doi.org/10.1186/s12984-021-00831-5
Lan, N., Hao, M., Niu, C. M., Cui, H., Wang, Y., Zhang, T., Fang, P., & Chou, C. (2021). Next-Generation Prosthetic Hand: from Biomimetic to Biorealistic. Research, 2021. https://doi.org/10.34133/2021/4675326
Lathouwers, E., Maricot, A., Tassignon, B., Geers, S., Flynn, L., Verstraten, T., & De Pauw, K. (2025). User accommodation to an active microprocessor-controlled knee in individuals with unilateral transfemoral amputation: a 5-week non-randomized trial. Journal of NeuroEngineering and Rehabilitation, 22(1). https://doi.org/10.1186/s12984-025-01637-5
Leccardi, M. J. A., & Ghezzi, D. (2020). Organic electronics for neuroprosthetics. Healthcare Technology Letters, 7(3), 52–57. https://doi.org/10.1049/htl.2019.0108
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.