Advancing Prosthetics with Artificial Intelligence
DOI:
https://doi.org/10.58445/rars.2060Keywords:
Artificial Intelligence (AI), ProstheticsAbstract
This paper explores the transformative impact of Artificial Intelligence (AI) on the development of prosthetic devices, focusing on advancements in real-time adaptation, responsiveness, and user experience through technologies such as microprocessors, neural interfacing, and Brain-Computer Interfaces (BCIs). It discusses key challenges in AI-powered prosthetics, including sensor accuracy, durability, and ethical concerns like privacy, employment impacts, and informed consent, while also analyzing socio-economic implications, particularly in healthcare equity and job markets. Anticipated future developments include enhanced sensory feedback, personalized designs via AI and 3D printing, and innovations in training and rehabilitation using Augmented Reality (AR) and Virtual Reality (VR). The paper emphasizes the importance of continued research and collaboration among engineers, healthcare professionals, and policymakers to ensure AI prosthetics are developed in alignment with societal values, promoting inclusivity and empowerment for individuals with disabilities.
References
Adewole, D. O., Serruya, M. D., Harris, J. P., Burrell, J. C., Petrov, D., Chen, H. I., ... Cullen, D. K. (2016). The evolution of neuroprosthetic interfaces. Critical Reviews in Biomedical Engineering, 44(1–2), 123–152. doi:10.1615/CritRevBiomedEng.2016017198
Bates, T. J., Fergason, J. R., & Pierrie, S. N. (2020). Technological advances in prosthesis design and rehabilitation following upper extremity limb loss. Current Reviews in Musculoskeletal Medicine, 13(4), 485–493. doi:10.1007/s12178-020-09656-6
Ferrero, L., Quiles, V., Ortiz, M., Iáñez, E., Gil-Agudo, Á., & Azorín, J. M. (2023). Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton. iScience, 26(5), 106675. doi:10.1016/j.isci.2023.106675
Fleming, A., Stafford, N., Huang, S., Hu, X., Ferris, D. P., & Huang, H. H. (2021). Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions. Journal of Neural Engineering, 18(4), 041004. doi:10.1088/1741-2552/ac1176
Furber, S. (2017). Microprocessors: the engines of the digital age. Proceedings. Mathematical, Physical, and Engineering Sciences, 473(2199), 20160893. doi:10.1098/rspa.2016.0893
Gavette, H., McDonald, C. L., Kostick-Quenet, K., Mullen, A., Najafi, B., & Finco, M. G. (2023). Advances in prosthetic technology: a perspective on ethical considerations for development and clinical translation. Frontiers in Rehabilitation Sciences, 4, 1335966. doi:10.3389/fresc.2023.1335966
Jayaraman, C., Mummidisetty, C. K., Albert, M. V., Lipschutz, R., Hoppe-Ludwig, S., Mathur, G., & Jayaraman, A. (2021). Using a microprocessor knee (C-Leg) with appropriate foot transitioned individuals with dysvascular transfemoral amputations to higher performance levels: a longitudinal randomized clinical trial. Journal of Neuroengineering and Rehabilitation, 18(1), 88. doi:10.1186/s12984-021-00879-3
Khan, F. H., Pasha, M. A., & Masud, S. (2021). Advancements in microprocessor architecture for ubiquitous AI-an overview on history, evolution, and upcoming challenges in AI implementation. Micromachines, 12(6), 665. doi:10.3390/mi12060665
Li, W., Shi, P., Li, S., & Yu, H. (2023). Current status and clinical perspectives of extended reality for myoelectric prostheses: review. Frontiers in Bioengineering and Biotechnology, 11, 1334771. doi:10.3389/fbioe.2023.1334771
Park, H. J. (2024). Patient perspectives on informed consent for medical AI: A web-based experiment. Digital Health, 10, 20552076241247938. doi:10.1177/20552076241247938
Qian, Y., Alhaskawi, A., Dong, Y., Ni, J., Abdalbary, S., & Lu, H. (2024). Transforming medicine: artificial intelligence integration in the peripheral nervous system. Frontiers in Neurology, 15, 1332048. doi:10.3389/fneur.2024.1332048
Sokołowska, B. (2023). Impact of virtual reality cognitive and motor exercises on brain health. International Journal of Environmental Research and Public Health, 20(5). doi:10.3390/ijerph20054150
Srinivasan, S., Vyas, K., McAvoy, M., Calvaresi, P., Khan, O. F., Langer, R., ... Herr, H. (2019). Polyimide electrode-based electrical stimulation impedes early stage muscle graft regeneration. Frontiers in Neurology, 10, 252. doi:10.3389/fneur.2019.00252
Varaganti, P., & Seo, S. (2024). Recent advances in biomimetics for the development of bio-inspired prosthetic limbs. Biomimetics (Basel, Switzerland), 9(5), 273. doi:10.3390/biomimetics9050273
Williams, S. C., Horsfall, H. L., Funnell, J. P., Hanrahan, J. G., Schaefer, A. T., Muirhead, W., & Marcus, H. J. (2022). Neurosurgical team acceptability of brain-computer interfaces: A two- stage international cross-sectional survey. World Neurosurgery, 164, e884–e898. doi:10.1016/j.wneu.2022.05.062
Yildiz, K. A., Shin, A. Y., & Kaufman, K. R. (2020). Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review. Journal of Neuroengineering and Rehabilitation, 17(1), 43. doi:10.1186/s12984-020-00667-5
Zbinden, J., Molin, J., & Ortiz-Catalan, M. (2024). Deep learning for enhanced prosthetic control: Real-time motor intent decoding for simultaneous control of artificial limbs. IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society, 32, 1177–1186. doi:10.1109/TNSRE.2024.3371896
Zhang, X., Ma, Z., Zheng, H., Li, T., Chen, K., Wang, X., ... Lin, H. (2020). The combination of brain- computer interfaces and artificial intelligence: applications and challenges. Annals of Translational Medicine, 8(11), 712. doi:10.21037/atm.2019.11.109
Boretti, A. (2023). A Perspective on 3D Printing in the Medical Field. ” Annals of 3D Printed Medicine. Annals of 3D Printed Medicine. (N.d.). Retrieved 28 November 2024, from http://Iopscience.Iop.Org/Article/10.1088/1742- 6596/1043/1/012003
Sanchez-Villamañan, M. D. C., Gonzalez-Vargas, J., Torricelli, D., Moreno, J. C., & Pons, J. L. (2019). Compliant lower limb exoskeletons: a comprehensive review on mechanical design principles. Journal of Neuroengineering and Rehabilitation, 16(1), 55. doi:10.1186/s12984-019-0517-9
US Department of Labor Unveils New Resource to Increase Competitive Integrated Employment for People with Disabilities. (2024).
Downloads
Posted
Categories
License
Copyright (c) 2024 Spoorthi Kakarla
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.