A classification method of hand EMG signals based on principal component analysis and artificial neural network

Autor:	Syahara U. Lekson, Khusnul A. Mustaqim, Wahyu Caesarendra, Augie Widyotriatmo, Andri R. Winoto
Rok vydání:	2016
Předmět:	Engineering Artificial neural network business.industry Speech recognition Principal component analysis Feature extraction Classification methods Pattern recognition Artificial intelligence business Hand movements
Zdroj:	2016 International Conference on Instrumentation, Control and Automation (ICA).
DOI:	10.1109/ica.2016.7811469
Popis:	This paper presents a classification method for multi-class classification of electromyography (EMG) signals from eight hand movements. The data were collected from 15 subjects. The EMG signals were extracted using 16 time-domain feature extraction methods. The 16 features are reduced using principal component analysis (PCA) to enhance the classification accuracy. The features results from PCA are classified using artificial neural network (ANN). The classification using ANN result to the training accuracy of 85.7% and the testing accuracy of 81.2%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::33abc7596ffa2eb16f3de6099cea0956 https://doi.org/10.1109/ica.2016.7811469 Zobrazit plný text záznamu