Review of research towards the myoelectric method of controlling bionic prosthesis

R. I. Bilyy
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Abstract

Myoelectric control of bionic prostheses is an important field of research in the field of rehabilitation. Intuitive and intelligent myoelectric control can restore upper limb function. However, much research now focuses on the development of various myoelectrical and biotechnical control methods, limiting research to the complex daily tasks of prosthetic manipulation, such as grasping and releasing. The article examines the latest advances in the research areas of bionic prosthesis management. In particular, attention is paid to the methods of determining movement intentions, classification of discrete movements, estimation of continuous movements, single-channel control, feedback control and combined control. Motor neurons group input signals from the central nervous system that affect muscles and form motor units. The electromyography (EMG) signal, which is obtained by recording motor neuron action potentials, reflects muscle activity. This signal, oscillating within ±5000 μV with a frequency of 6 to 500 Hz, reflects the characteristics of muscle contraction. Depending on the location of the sensors, EMG signals are divided into intramuscular and surface electromyography. Intramuscular electromyography provides an accurate study of muscle activation, but requires the implantation of sensors, which can lead to physical problems. EMG, which captures a signal from the surface of the skin, is easier to use and is widely used in experiments with myoelectric prostheses.
控制仿生假体的肌电方法研究综述
仿生假肢的肌电控制是康复领域的一个重要研究领域。直观、智能的肌电控制可以恢复上肢功能。然而,目前很多研究都集中在各种肌电和生物技术控制方法的开发上,研究范围仅限于假肢操作的复杂日常任务,如抓取和释放。本文探讨了仿生假肢管理研究领域的最新进展。其中特别关注了确定运动意图、离散运动分类、连续运动估计、单通道控制、反馈控制和组合控制的方法。运动神经元将来自中枢神经系统的输入信号分组,影响肌肉并形成运动单元。通过记录运动神经元动作电位获得的肌电图(EMG)信号可反映肌肉活动。该信号在 ±5000 μV 范围内振荡,频率为 6 至 500 Hz,反映了肌肉收缩的特征。根据传感器的位置,肌电信号可分为肌内肌电图和表面肌电图。肌内肌电图可准确研究肌肉的激活情况,但需要植入传感器,这可能会导致物理问题。从皮肤表面捕捉信号的肌电图更易于使用,被广泛用于肌电假肢的实验中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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