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Author:

Dong, M. (Dong, M..) | Wang, Z. (Wang, Z..) | Li, J. (Li, J..) | Rong, X. (Rong, X..) | Zhou, Y. (Zhou, Y..) | Jiao, R. (Jiao, R..)

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EI Scopus

Abstract:

Muscle strength training is to reduce muscle atrophy and increase muscle strength. For ankle joint, we developed six robot-assisted muscle strength training modes, namely, continuous passive motion (CPM), isotonic, isometric, isokinetic, centripetal and centrifugal exercise. The paper aims to evaluate the performance of different robot-assisted training modes based on angle/torque and surface electromyography (sEMG) characteristics. The sEMG characteristics were used to extract the features of the degree of muscle activation, while the angle/torque information were used to calculate the endurance ratio (ER), peak torque (PT) and torque acceleration energy (TAE). Experimental results showed that the level of the degree of muscle activation, PT and TAE in CPM were low, and ER was about 1. For isotonic exercise, isometric exercise and isokinetic exercise, most of the evaluation indexes including the degree of muscle activation, PT and TAE increased significantly compared with CPM (For isotonic exercise, ER is 1.09 ± 0.25, $r$ (LG) is 0.0538 ± 0.0171, $r$ (MG) is 0.0430 ± 0.0116, $r$ (SO) is 0.0642 ± 0.0178; For isometric exercise, PT is 0.72 ± 0.16, TAE is 2.15 ± 0.51, $r$ (TA) is 0.0532 ± 0.0256; For isokinetic exercise, PT is 0.72 ± 0.16, TAE is 1.99 ± 0.82). The results reveal the different performances among different modes, thus lay a foundation for exploring subject-specific adaptive ankle muscle strength training strategies. © 2018 IEEE.

Keyword:

Ankle muscle strength training angle/torque information degree of muscle activation evaluation

Author Community:

  • [ 1 ] [Dong M.]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing, 100124, China
  • [ 2 ] [Wang Z.]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing, 100124, China
  • [ 3 ] [Li J.]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing, 100124, China
  • [ 4 ] [Rong X.]The Affiliated Hospital of Qingdao University, Department of Neurology, Qingdao, 266000, China
  • [ 5 ] [Zhou Y.]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing, 100124, China
  • [ 6 ] [Jiao R.]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing, 100124, China

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Source :

IEEE Transactions on Medical Robotics and Bionics

ISSN: 2576-3202

Year: 2023

Issue: 2

Volume: 5

Page: 401-410

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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