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Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation

机译:sEMG信号持续识别双侧肘关节屈伸的初步研究

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摘要

Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range.
机译:表面肌电图(sEMG)信号与人体肌肉的激活和人体的运动密切相关,可用于估计康复领域中人体四肢的动态。它们还具有用于双侧康复的潜力,其中偏瘫患者可以通过一些康复设备跟随未受影响的肢体运动来训练其患肢。处理sEMG的传统方法侧重于运动模式识别,即离散模式,这不适用于双边康复。为了克服这个问题,在本文中,我们建立了sEMG信号与人体在矢状面弯曲和伸展时的运动之间的关系。在进行的实验中,需要四名参与者在手中不使用前臂动力学的情况下,仅用手中的惯性传感器平稳地在矢状面上进行肘部弯曲和伸展。在这种情况下,与高负载或高加速度的信号相比,sEMG信号较弱。对比实验结果表明,也可以在可接受的精度范围内获得连续运动。

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