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基于肌电图的上肢肘关节矢状面角度定量表示方法

Electromyography-Based Quantitative Representation Method for Upper-Limb Elbow Joint Angle in Sagittal Plane.

作者信息

Pang Muye, Guo Shuxiang, Huang Qiang, Ishihara Hidenori, Hirata Hideyuki

机构信息

Graduate School of Engineering, Kagawa University, Takamatsu, 761-0396 Japan.

Department of Intelligent Mechanical Systems Engineering, Kagawa University, Takamatsu, 761-0396 Japan ; School of Life Science and Technology, Beijing Institute of Technology, Beijing, 100081 China.

出版信息

J Med Biol Eng. 2015;35(2):165-177. doi: 10.1007/s40846-015-0033-8. Epub 2015 Apr 25.

Abstract

This paper presents a quantitative representation method for the upper-limb elbow joint angle using only electromyography (EMG) signals for continuous elbow joint voluntary flexion and extension in the sagittal plane. The dynamics relation between the musculotendon force exerted by the biceps brachii muscle and the elbow joint angle is developed for a modified musculoskeletal model. Based on the dynamics model, a quadratic-like quantitative relationship between EMG signals and the elbow joint angle is built using a Hill-type-based muscular model. Furthermore, a state switching model is designed to stabilize the transition of EMG signals between different muscle contraction motions during the whole movement. To evaluate the efficiency of the method, ten subjects performed continuous experiments during a 4-day period and five of them performed a subsequent consecutive stepping test. The results were calculated in real-time and used as control reference to drive an exoskeleton device bilaterally. The experimental results indicate that the proposed method can provide suitable prediction results with root-mean-square (RMS) errors of below 10° in continuous motion and RMS errors of below 10° in stepping motion with 20° and 30° increments. It is also easier to calibrate and implement.

摘要

本文提出了一种仅使用肌电图(EMG)信号来定量表示上肢肘关节角度的方法,用于在矢状面内连续进行肘关节的主动屈伸。针对改进的肌肉骨骼模型,推导了肱二头肌施加的肌腱力与肘关节角度之间的动力学关系。基于该动力学模型,使用基于希尔型的肌肉模型建立了EMG信号与肘关节角度之间类似二次函数的定量关系。此外,设计了一种状态切换模型,以稳定整个运动过程中不同肌肉收缩运动之间EMG信号的转换。为了评估该方法的有效性,10名受试者在4天内进行了连续实验,其中5人随后进行了连续的踏步测试。结果实时计算,并用作双侧驱动外骨骼装置的控制参考。实验结果表明,该方法在连续运动中能提供合适的预测结果,均方根(RMS)误差低于10°,在踏步运动中,步幅增量为20°和30°时,RMS误差也低于10°。而且该方法更容易校准和实现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5248/4414936/f3f84f3f0eaa/40846_2015_33_Fig1_HTML.jpg

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