How Deep Learning and Neural Networks can Improve Prosthetics and Exoskeletons: A Review of State-of-the-Art Methods and Challenges
Abstract
Deep learning and neural networks are powerful computational methods that have been widely applied in various fields, such as healthcare and robotics. In this paper, we review some of the recent research studies that use deep learning and neural networks in healthcare and robotics, particularly focusing on their application in prosthetics and exoskeletons. The main source of data for this review is Scopus, which is a large and multidisciplinary database of peer-reviewed literature. The search criteria for this review are exoskeleton AND prosthetic AND deep AND learning. The search is limited to documents published from 2014 to 2023, as this period covers the recent developments and trends in the field4. The search results in 488 documents that match the criteria. We selected 20 papers that represent the state-of-the-art methods and applications of deep learning and neural networks in prosthetics and exoskeletons. We categorized these papers by various attributes, such as document type, subject area, sensor type, respondent, condition, etc. The main finding of this paper was that deep learning techniques and neural networks have diverse and transformative potential in healthcare and robotics, especially in the development and improvement of prosthetics and exoskeletons. The paper highlighted how these advanced computational methods can be harnessed to interpret complex biological signals, improve device functionality, enhance user safety, and ultimately improve quality of life for individuals using these devices. The paper also identified some possible future directions for this topic, such as exploring the impact of deep learning techniques and neural networks on the performance, usability, and user satisfaction of prosthetics and exoskeletons. This paper provided a valuable insight into the current state-of-the-art and future prospects of deep learning techniques and neural networks in healthcare and robotics
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References
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[25] R. Byfield, M. Guess, K. Sattari, Y. Xie, T. Guess, and J. Lin, “Machine learning full 3-D lower-body kinematics and kinetics on patients with osteoarthritis from electromyography,” Biomed. Eng. Adv., vol. 5, p. 100088, 2023, doi: https://doi.org/10.1016/j.bea.2023.100088.
[26] D. Buongiorno et al., “Deep learning for processing electromyographic signals: A taxonomy-based survey,” Neurocomputing, vol. 452, pp. 549–565, 2021, doi: https://doi.org/10.1016/j.neucom.2020.06.139.
[27] Y. Zhou, X. Lü, Z. Wang, Z. Huang, J. Yang, and X. Zhao, “Surface myoelectric signals decoding using the continuous wavelet transform singularity detection,” Proc. 2011 Int. Symp. Bioelectron. Bioinformatics, ISBB 2011, pp. 191–194, 2011, doi: 10.1109/ISBB.2011.6107678.
[28] J. Fan, L. Vargas, D. G. Kamper, and X. Hu, “Robust neural decoding for dexterous control of robotic hand kinematics,” Comput. Biol. Med., vol. 162, p. 107139, 2023, doi: https://doi.org/10.1016/j.compbiomed.2023.107139.
[29] T. Zhou, Y. Wang, Q. Zhu, and J. Du, “Human hand motion prediction based on feature grouping and deep learning: Pipe skid maintenance example,” Autom. Constr., vol. 138, p. 104232, 2022, doi: https://doi.org/10.1016/j.autcon.2022.104232.
[30] Z. U. Xiaoqi, L. I. Yun, and Z. Qianxiang, “Evaluation of Muscle Fatigue Based on Surface Electromyography and Subjective Assessment.” pp. 2003–2006, 2006.
[31] Z. Lu, K. Tong, H. Shin, S. Li, and P. Zhou, “Advanced myoelectric control for robotic hand-assisted training: outcome from a stroke patient,” Front. Neurol., vol. 8, p. 107, 2017.
[32] C. Camardella, M. Barsotti, D. Buongiorno, A. Frisoli, and V. Bevilacqua, “Towards online myoelectric control based on muscle synergies-to-force mapping for robotic applications,” Neurocomputing, vol. 452, pp. 768–778, 2021, doi: https://doi.org/10.1016/j.neucom.2020.08.081.
[33] M. Sharbafi, A. Naseri, A. Seyfarth, and M. Grimmer, “Chapter 7 - Neural control in prostheses and exoskeletons,” H. Dallali, E. Demircan, and M. B. T.-P. P. Rastgaar, Eds., Academic Press, 2020, pp. 153–178. doi: https://doi.org/10.1016/B978-0-12-817450-0.00007-9.
[34] T. Das, L. Gohain, N. M. Kakoty, M. B. Malarvili, P. Widiyanti, and G. Kumar, “Hierarchical approach for fusion of electroencephalography and electromyography for predicting finger movements and kinematics using deep learning,” Neurocomputing, vol. 527, pp. 184–195, 2023, doi: https://doi.org/10.1016/j.neucom.2023.01.061.
[35] A. Rezaie Zangene et al., “An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running,” Biomed. Signal Process. Control, vol. 86, p. 105103, 2023, doi: https://doi.org/10.1016/j.bspc.2023.105103.
[36] Z. Khademi, F. Ebrahimi, and H. M. Kordy, “A review of critical challenges in MI-BCI: From conventional to deep learning methods,” J. Neurosci. Methods, vol. 383, p. 109736, 2023, doi: https://doi.org/10.1016/j.jneumeth.2022.109736.
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Published
2023-10-08
How to Cite
[1]
T. Triwiyanto, W. Caesarendra, A. A. Ahmed, and A. V.H, “How Deep Learning and Neural Networks can Improve Prosthetics and Exoskeletons: A Review of State-of-the-Art Methods and Challenges ”, j.electron.electromedical.eng.med.inform, vol. 5, no. 4, pp. 277-289, Oct. 2023.
Section
Electronics
Copyright (c) 2023 Triwiyanto Triwiyanto, Wahyu Caesarendra, Abdussalam Ali Ahmed, Abdullayev V.H
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