Work

Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband

Year: 2023

Type: article

Abstract: The deployment of electromyography (EMG) signals attracts many researchers since it can be used in decoding finger movements for exoskeleton robotics, prosthetics hand, and powered wheelchair. However, decoding any movement is a challenging task. The success of EMG signals' use lies in the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study evaluates an eight-feature extraction evaluation on various machine learnings such as the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). The dataset from four intact subjects is used to classify twelve finger movements. Through 5 cross-validations, the result shows that almost all feature extractions combined with SVM outperform other combinations of features and classifiers. Mean Absolute Value (MAV) as a feature and SVM as a classifier highlight the best combination with an accuracy of 94.01%.

Source: Journal of Engineering and Technological Sciences

DOI: 10.5614/j.eng.technol.sci.2023.55.5.8

Authors: Khairul Anam, Harun Ismail, Faruq Sandi Hanggara, Cries Avian, Safri Nahela, Muchamad Arif Hana Sasono

Institutions: Universitas Jember, National Taiwan University of Science and Technology

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: gold

Estimated APC paid: $150