Abstract
High-risk diseases such as stroke can do great harm to human hands. Hand rehabilitation for stroke patients is a complex and necessary task. To achieve this goal, this paper introduces a hand exoskeleton equipment with flexible joints and EMG-base motion prediction. Experiment of the equipment includes kinematics analysis, EMG signal detection by MYO armband and motion prediction base on BP neural network. The result shows that the device can not only assists patient bending or extending fingers, but also perform six kinds of rehabilitation exercises with 92% accuracy for target motion recognition.
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Acknowledgement
This paper is supported by the Primary Research & Development Program of Jiangsu Province (Grant No. BE2015701), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20170898), the Natural Science Foundation of Higher Education Institutions of Jiangsu Province, China (Grant No. 16KJB460017), and the NUPTSF (Grant No. NY215050, No. NY218027 and No. 2018XZZ06).
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Zhang, J., Dai, J., Chen, S., Xu, G., Gao, X. (2019). Design of Finger Exoskeleton Rehabilitation Robot Using the Flexible Joint and the MYO Armband. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11745. Springer, Cham. https://doi.org/10.1007/978-3-030-27529-7_19
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DOI: https://doi.org/10.1007/978-3-030-27529-7_19
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