Forecasting the Semg Signal Using Wavelet Transform and Anfis Model

IF 0.8 4区 综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES
Tanu Sharma, K. P. Sharma
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引用次数: 0

Abstract

In this paper we study how the muscles in the human body move, electromyography (EMG Signal) is employed as a diagnostic technique for identifying various muscular activity. Noise from the SEMG signal is effectively minimized with a suitable wavelet selection. The root mean square values have been evaluated to determine which wavelet is the most efficient for signal denoising. Further, since a learning method of a neural structure with connections based on rules is necessary to be able to estimate the relationship, this paper also aims to analyse an approach that uses signals obtained by surface electrodes to characterize hand movements of the human arm for pattern recognition (i.e. ANFIS method is employed). The characteristics of seven hand gestures are categorized using the ANFIS-based learning, which is then assessed in order to predict the link between input and output.

Abstract Image

利用小波变换和 Anfis 模型预测 Semg 信号
在本文中,我们研究了人体肌肉的运动方式,并将肌电图(EMG 信号)作为一种诊断技术,用于识别各种肌肉活动。通过选择合适的小波,可以有效地将 SEMG 信号的噪声降至最低。通过评估均方根值,可以确定哪种小波对信号去噪最有效。此外,由于基于规则连接的神经结构的学习方法是估算关系的必要条件,本文还旨在分析一种利用表面电极获得的信号来描述人类手臂手部动作以进行模式识别的方法(即采用 ANFIS 方法)。使用基于 ANFIS 的学习方法对七种手势的特征进行分类,然后对其进行评估,以预测输入和输出之间的联系。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
37
审稿时长
>12 weeks
期刊介绍: To promote research in all the branches of Science & Technology; and disseminate the knowledge and advancements in Science & Technology
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