Estimation of Upper Limb Joint Angle Using Surface EMG Signal

计算机科学 接头(建筑物) 信号(编程语言) 肘部 扭矩 人工智能 肌电图 上肢 机器人 模拟 计算机视觉 物理医学与康复 医学 解剖 工程类 建筑工程 物理 程序设计语言 热力学
作者
Yee Mon Aung,Adel Al-Jumaily
出处
期刊:International Journal of Advanced Robotic Systems [SAGE Publishing]
卷期号:10 (10): 369-369 被引量:60
标识
DOI:10.5772/56717
摘要

In the development of robot-assisted rehabilitation systems for upper limb rehabilitation therapy, human electromyogram (EMG) is widely used due to its ability to detect the user intended motion. EMG is one kind of biological signal that can be recorded to evaluate the performance of skeletal muscles by means of a sensor electrode. Based on recorded EMG signals, user intended motion could be extracted via estimation of joint torque, force or angle. Therefore, this estimation becomes one of the most important factors to achieve accurate user intended motion. In this paper, an upper limb joint angle estimation methodology is proposed. A back propagation neural network (BPNN) is developed to estimate the shoulder and elbow joint angles from the recorded EMG signals. A Virtual Human Model (VHM) is also developed and integrated with BPNN to perform the simulation of the estimated angle. The relationships between sEMG signals and upper limb movements are observed in this paper. The effectiveness of our developments is evaluated with four healthy subjects and a VHM simulation. The results show that the methodology can be used in the estimation of joint angles based on EMG.
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