Work

Analysis of Extracted Forearm sEMG Signal Using LDA, QDA, K-NN Classification Algorithms

Year: 2014

Type: article

Abstract: A surface electromyographic (sEMG) signal includes important information on muscular activity and was recently widely used as an input signal in a myoelectric control system.In this manuscript, eight hand motions were classified using different extracted features from sEMG signals.The results of the experiment show that the combination of sample entropy (SampEnt), root mean square (RMS), myopulse percentage rate (MYOP), and difference absolute standard deviation value (DASDV) achieved the highest classification rate of 98.56% using the linear discriminant analysis (LDA) classifier.Moreover, this study investigated the best value of K that should be used as an input parameter in the K-nearest neighbor (K-NN) algorithm.The result demonstrates that k = 5 is the optimal choice in most cases.

Source: The Open Automation and Control Systems Journal

DOI: 10.2174/1874444301406010108

Authors: Firas AlOmari, Gunhai Liu

Institutions: Jiangsu University, Bentham Science Publishers (United Arab Emirates)

Language: English

Topic: Muscle activation and electromyography studies

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

OpenAlex ID: W2013983996