Hand Electromyography Circuit and Signals Classification Using Artificial Neural Network

Muhammad Shahzaib, S. Shakil
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引用次数: 4

Abstract

Electromyography (EMG) is the study of electrical activity of muscles signals. This technique can be used for the control of prosthetic for amputees or for medical purposes in muscular disorders. Major challenge faced in this domain is high cost of the devices to control the prosthetic. In addition to the cost of the device, number of parameters used for classification is large for studies in this domain. In this study we propose a low cost circuit for EMG signal extraction. We used 4 channels of proposed EMG circuit to classify 6 different motion that includes individual finger motions and fist motion. Despite being low cost, our circuit provides the signals that can be classified with high accuracies comparable to other studies. For classification, we used artificial neural network with less number of parameters to achieve accuracies comparable to other studies using higher number of parameters. We collected data from 5 healthy subjects using our proposed circuit. Behavior of EMG signal varies from subject to subject depending upon different factors. We used six features from time and frequency domains, gave an accuracy of 98.8% and 96.8% for all combined subjects with two different algorithms and an average accuracy of 99% with standard deviation of 0.6 for all individual subjects.
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基于人工神经网络的手部肌电图电路及信号分类
肌电图(EMG)是对肌肉电信号的电活动的研究。该技术可用于截肢者假肢的控制或用于肌肉疾病的医疗目的。该领域面临的主要挑战是控制假肢的设备成本高。除了设备的成本之外,用于分类的参数数量对于该领域的研究来说是很大的。在本研究中,我们提出了一种低成本的肌电信号提取电路。我们使用4个通道的EMG电路对6种不同的运动进行分类,包括单个手指运动和拳头运动。尽管成本低,但我们的电路提供的信号可以与其他研究相比具有较高的分类精度。对于分类,我们使用了参数数量较少的人工神经网络,以达到与其他使用更多参数的研究相当的精度。我们使用我们提出的电路收集了5名健康受试者的数据。肌电图信号的表现因不同的因素而异。我们使用了来自时域和频域的六个特征,使用两种不同的算法对所有组合受试者的准确率分别为98.8%和96.8%,对所有单个受试者的平均准确率为99%,标准差为0.6。
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