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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part H. Journal of Engineering in Medicine >Real-time controller for foot-drop correction by using surface electromyography sensor
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Real-time controller for foot-drop correction by using surface electromyography sensor

机译：使用表面肌电传感器实时进行脚部矫正的控制器

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摘要

Foot drop is a disease caused mainly by muscle paralysis, which incapacitates the nerves generating the impulses that control feet in a heel strike. The incapacity may stem from lesions that affect the brain, the spinal cord, or peripheral nerves. The foot becomes dorsiflexed, affecting normal walking. A design and analysis of a controller for such legs is the subject of this article. Surface electromyography electrodes are connected to the skin surface of the human muscle and work on the mechanics of human muscle contraction. The design uses real surface electromyography signals for estimation of the joint angles. Various-speed flexions and extensions of the leg were analyzed. The two phases of the design began with surface electromyography of real human leg electromyography signal, which was subsequently filtered, amplified, and normalized to the maximum amplitude. Parameters extracted from the surface electromyography signal were then used to train an artificial neural network for prediction of the joint angle. The artificial neural network design included various-speed identification of the electromyography signal and estimation of the angles of the knee and ankle joints by a recognition process that depended on the parameters of the real surface electromyography signal measured through real movements. The second phase used artificial neural network estimation of the control signal, for calculation of the electromyography signal to be stimulated for the leg muscle to move the ankle joint. Satisfactory simulation(MATLAB/Simulink version 2012a) and implementation results verified the design feasibility.
机译：脚掉落是一种主要由肌肉麻痹引起的疾病，肌肉麻痹使神经丧失能力，产生了在脚后跟打击中控制脚的冲动。丧失能力可能源于影响大脑，脊髓或周围神经的病变。脚背屈，影响正常行走。本文的主题是针对此类支脚的控制器的设计和分析。表面肌电图电极连接到人体肌肉的皮肤表面，并在人体肌肉收缩的机制上起作用。该设计使用真实的表面肌电信号来估计关节角度。分析了腿的各种速度的弯曲和伸展。设计的两个阶段从真实的人腿肌电信号的表面肌电图开始，随后对其进行滤波，放大并归一化为最大幅度。然后将从表面肌电信号中提取的参数用于训练人工神经网络，以预测关节角度。人工神经网络设计包括对肌电信号的各种速度识别以及通过识别过程估算膝盖和踝关节的角度，该识别过程取决于通过真实运动测得的真实表面肌电信号的参数。第二阶段使用控制信号的人工神经网络估计，以计算要刺激腿部肌肉移动踝关节的肌电信号。令人满意的仿真（MATLAB / Simulink 2012a版）和实施结果证明了设计的可行性。

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