Recent Advances in EMG Pattern Recognition for Prosthetic Control.
作者信息
A Ameri, M Ghorbani
机构信息
PhD, Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
出版信息
J Biomed Phys Eng. 2020 Apr 1;10(2):129-130. doi: 10.31661/jbpe.v0i0.2002-1076. eCollection 2020 Apr.
Abstract
摘要
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本文引用的文献
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Real-time, simultaneous myoelectric control using a convolutional neural network.基于卷积神经网络的实时同步肌电控制。
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Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.基于卷积神经网络的深度学习应用于肌电图数据：一种用于假手运动分类的资源。
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A comparison between force and position control strategies in myoelectric prostheses.肌电假肢中力控制策略与位置控制策略的比较。
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Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.用于控制动力上肢假肢的肌电图模式识别：现状与临床应用面临的挑战
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A robust, real-time control scheme for multifunction myoelectric control.一种用于多功能肌电控制的强大实时控制方案。
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A new strategy for multifunction myoelectric control.一种用于多功能肌电控制的新策略。
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