Abstract
The training is very important for the application of electromyography (EMG) prosthesis. Because the traditional training with physical prostheses is inefficient and boring, the virtual training system, which has natural advantages in terms of intuitiveness and interactivity, is more widely used. In this study, a virtual training system for intelligent upper limb prosthesis with bidirectional neural channels has been developed. The training system features motion and sensation neural interaction, which is realized by an EMG control module and sense feedback module based on vibration stimulation. A Human-machine closed-loop interaction training based on the virtual system is studied. The experiments are carried out, and the effectiveness of the virtual system in shortening the training time and improving the operation ability of prosthesis has been verified.
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References
Dhillon, G.S., Horch, K.W.: Direct neural sense feedback and control of a prosthetic arm. IEEE Trans. Neural Syst. Rehabil. Eng. 13(4), 468–472 (2015)
Perry, B.N., Armiger, R.S., Yu, K.E., et al.: Virtual integration environment as an advanced prosthetic limb training platform. Front. Neurol. 9, 785 (2018)
Wang, C.W., Gao, W., Wang, X.R.: The theory, implementation and application of virtual reality technology. Tsinghua university, Beijing (1996)
Armiger, R.S., Tenore, F.V., Bishop, W.E., et al.: A real-time virtual integration environment for neuroprosthetics and rehabilitation. J. Hopkins APL Tech. Dig. 30(3), 198–206 (2011)
Linda, R., Katherine, E., Lieberman, K.S., Charies, K.: Using virtual reality environment to facilitate training with advanced upper-limb prosthesis. J. Rehabil. Res. Dev. 48(6), 707–718 (2011)
Chau, B., Phelan, I., Ta, P., et al.: Immersive virtual reality therapy with myoelectric control for treatment-resistant phantom limb pain: case report. Innov. Clin. Neurosci. 14(7–8), 3–7 (2017)
Ortiz, C.M.: Phantom motor execution facilitated by machine learning and augmented reality as treatment for phantom limb pain: a single group, clinical trial in patients with chronic intractable phantom limb pain. Lancet 388(10062), 2885 (2016)
Johannes, M.S., Bigelow, J.D., Burck, J.M., et al.: An overview of the developmental process for the modular prosthetic limb. J. Hopkins APL Tech. Dig. 30(3), 207–216 (2011)
Hsiu, H., Ulrich, R., Shu, L.: Investigating learners’ attitudes toward virtual reality learning environments: based on a constructivist approach. Comput. Educ. 55, 1171–1182 (2010)
Gu, Y., Yang, D., Huang, Q., et al.: Robust EMG pattern recognition in the presence of confounding factors: features, classifiers and adaptive learning. Expert Syst. Appl. 96, 208–217 (2018)
Zeng, B., Fan, S., Jiang, L., Liu, H.: Design and experiment of a modular multisensory hand for prosthetic applications. Ind. Robot: Int. J. 44(1), 104–113 (2017)
Yang, D., Jiang, L., Zhang, X., Liu, H.: Simultaneous estimation of 2-DOF wrist movements based on constrained non-negative matrix factorization and Hadamard product. Elsevier Ltd. (2020)
Taylor, C.L., Schwarz, R.J.: The anatomy and mechanics of the human hand. Artif. Limbs 2, 22–35 (1955)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (No. 91948302 and No. 51875120).
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Hu, Y., Jiang, L., Yang, B. (2021). Research on Virtual Training System for Intelligent Upper Limb Prosthesis with Bidirectional Neural Channels. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13015. Springer, Cham. https://doi.org/10.1007/978-3-030-89134-3_29
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DOI: https://doi.org/10.1007/978-3-030-89134-3_29
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