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Musculoskeletal modeling and control of the lower limb in cycling using an optimal central pattern generator

Haghpanah, A ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1007/s40997-022-00566-1
  3. Publisher: Institute for Ionics , 2023
  4. Abstract:
  5. Musculoskeletal modeling of human motions such as cycling can help rehabilitation experts design and develop more effective therapy procedures. In the current study, a musculoskeletal model for the lower limb of cyclists is developed and controlled by designing a CPG network. EMG sensors were attached to the leg of five persons with different physical conditions, including professional and amateur cyclists, to record six muscles’ activities. Also, the joints’ trajectories have been measured. The skeletal dynamics of the skeleton of the lower limb are modeled using Lagrangian mechanics, and Hill’s modified nonlinear model is used for modeling the muscles. Central pattern generators are responsible for producing rhythmic motions in the human body, so to simulate and control the musculoskeletal model, a six-neuron CPG network is proposed and optimized by genetic algorithm. Simulations results show the RMSEs in tracking the joint trajectories as 0.0461, 0.0609, 0.1495, respectively, for the hip, the knee, and the ankle joint. The RMSEs for the VM, GT, BF, VL, TA and RF muscles are resulted as 0.0204, 0.0104, 0.0326, 0.0198, 0.0622, 0.002. Results show the good performance of the CPG as the controller of the central nervous system and also qualify this framework to be used in future studies. © 2023, The Author(s), under exclusive licence to Shiraz University
  6. Keywords:
  7. CPG ; Genetic algorithm ; Hill model ; Matsuoka oscillator
  8. Source: Iranian Journal of Science and Technology - Transactions of Mechanical Engineering ; Volume 47, Issue 3 , 2023 , Pages 1121-1130 ; 22286187 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s40997-022-00566-1