Wavelet Based Classification of Finger Movements with Machine Learning Classifier

被引：0

作者：

Karuna, M. ^{[1
]}

Rao, Ippili Krishna ^{[2
]}

Kumar, M. Vijay ^{[3
]}

Guntur, Sitaramanjaneya Reddy ^{[4
]}

机构：

[1] Vignans Inst Informat Technol, Dept Elect & Commun Engn, Visakhapatnam, Andhra Pradesh, India

[2] Raghu Engn Coll, Dept Elect & Commun Engn, Visakhapatnam, Andhra Pradesh, India

[3] Christian Med Coll & Hosp, Dept Distance Educ, Vellore, Tamil Nadu, India

[4] Vignans Fdn Sci Technol & Res, Dept Biomed Engn, Guntur, Andhra Pradesh, India

来源：

2022 SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRICAL, COMPUTING, COMMUNICATION AND SUSTAINABLE TECHNOLOGIES (ICAECT) | 2022年

关键词：

Classification; Decomposition; EMG; Feature Extraction; Wavelets;

D O I：

10.1109/ICAECT54875.2022.9808004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Feature extraction and machine learning methods are essential in electromyogram (EMG) based movement discrimination for controlling prosthesis. The purpose of the research is to establish finger movements for an upper-limb prosthesis. In this paper, research looks at how to build an intelligent classification system for finger gestures. Electromyogram (EMG) impulses from a person's muscles can be used to movements control of numerous modern prosthesis. This paper studies the accuracy of finger movement detection using surface EMG data. Across a wide range of subjects, the proposed technique produced results with an average classification accuracy of 86.18 percent.

引用

页数：4

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