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一种用于肘关节肌肉扭矩估计的改进型肌电图驱动神经肌肉骨骼模型。

An Improved EMG-Driven Neuromusculoskeletal Model for Elbow Joint Muscle Torque Estimation.

作者信息

Hu Bingshan, Tao Haoran, Lu Hongrun, Zhao Xiangxiang, Yang Jiantao, Yu Hongliu

机构信息

Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China.

Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, China.

出版信息

Appl Bionics Biomech. 2021 Oct 31;2021:1985741. doi: 10.1155/2021/1985741. eCollection 2021.

Abstract

The accurate measurement of human joint torque is one of the research hotspots in the field of biomechanics. However, due to the complexity of human structure and muscle coordination in the process of movement, it is difficult to measure the torque of human joints in vivo directly. Based on the traditional elbow double-muscle musculoskeletal model, an improved elbow neuromusculoskeletal model is proposed to predict elbow muscle torque in this paper. The number of muscles in the improved model is more complete, and the geometric model is more in line with the physiological structure of the elbow. The simulation results show that the prediction results of the model are more accurate than those of the traditional double-muscle model. Compared with the elbow muscle torque simulated by OpenSim software, the Pearson correlation coefficient of the two shows a very strong correlation. One-way analysis of variance (ANOVA) showed no significant difference, indicating that the improved elbow neuromusculoskeletal model established in this paper can well predict elbow muscle torque.

摘要

人体关节扭矩的精确测量是生物力学领域的研究热点之一。然而,由于人体在运动过程中结构和肌肉协调的复杂性,直接在体内测量人体关节扭矩具有一定难度。本文基于传统的肘部双肌肉骨骼模型,提出了一种改进的肘部神经肌肉骨骼模型来预测肘部肌肉扭矩。改进模型中的肌肉数量更完整,几何模型更符合肘部的生理结构。仿真结果表明,该模型的预测结果比传统双肌肉模型更准确。与OpenSim软件模拟的肘部肌肉扭矩相比,两者的Pearson相关系数显示出很强的相关性。单因素方差分析(ANOVA)表明无显著差异,这表明本文建立的改进肘部神经肌肉骨骼模型能够很好地预测肘部肌肉扭矩。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3d8/8572603/36ada924b71c/ABB2021-1985741.001.jpg

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