Computational simulation for lifting motion of musculoskeletal arm

Zhao, Yongkun; Zhang, Mingquan; Ding, Shihang; Todoh, Masahiro; Feng, Lijie

doi:10.1109/ccdc55256.2022.10033644

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The TB is composed of three muscles, which are the triceps lateral head (TRIlat), triceps long head (TRIlong), and triceps medial head (TRImed). This musculoskeletal arm model is validated in our previous paper [ 16 , 22 ]. The anatomy drawings and schematic diagrams of the arm are illustrated in Figure 1 .…”

Section: Musculoskeletal Arm Modelmentioning

confidence: 99%

“…The PD feedback gains have been demonstrated to be the natural feedback control policy adopted by the human brain [ 20 , 21 ]. While the fuzzy system is to imitate that the brain cannot accurately calculate the distance between the start and target position, instead, it calculates the distance in a fuzzy manner [ 22 ]. For the neural networks, it aims to decrease the energy consumption during performing the arm reaching movements by making the skeletal muscles generate relatively small tensile forces.…”

Section: Neural Feedback Controllermentioning

confidence: 99%

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Neuromechanics-Based Neural Feedback Controller for Planar Arm Reaching Movements

Zhao

Zhang

et al. 2023

Bioengineering

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Based on the principles of neuromechanics, human arm movements result from the dynamic interaction between the nervous, muscular, and skeletal systems. To develop an effective neural feedback controller for neuro-rehabilitation training, it is important to consider both the effects of muscles and skeletons. In this study, we designed a neuromechanics-based neural feedback controller for arm reaching movements. To achieve this, we first constructed a musculoskeletal arm model based on the actual biomechanical structure of the human arm. Subsequently, a hybrid neural feedback controller was developed that mimics the multifunctional areas of the human arm. The performance of this controller was then validated through numerical simulation experiments. The simulation results demonstrated a bell-shaped movement trajectory, consistent with the natural motion of human arm movements. Furthermore, the experiment testing the tracking ability of the controller revealed real-time errors within one millimeter, with the tensile force generated by the controller’s muscles being stable and maintained at a low value, thereby avoiding the issue of muscle strain that can occur due to excessive excitation during the neurorehabilitation process.

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Section: Musculoskeletal Arm Modelmentioning

confidence: 99%