Khairul Anam, Harun Ismail, F. S. Hanggara, Cries Avian, Safri Nahela, Muchamad Arif Hana Sasono
{"title":"使用双妙臂章对用于手指运动分类的各种机器学习方法进行特征提取评估","authors":"Khairul Anam, Harun Ismail, F. S. Hanggara, Cries Avian, Safri Nahela, Muchamad Arif Hana Sasono","doi":"10.5614/j.eng.technol.sci.2023.55.5.8","DOIUrl":null,"url":null,"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%.","PeriodicalId":15689,"journal":{"name":"Journal of Engineering and Technological Sciences","volume":" 60","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband\",\"authors\":\"Khairul Anam, Harun Ismail, F. S. Hanggara, Cries Avian, Safri Nahela, Muchamad Arif Hana Sasono\",\"doi\":\"10.5614/j.eng.technol.sci.2023.55.5.8\",\"DOIUrl\":null,\"url\":null,\"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%.\",\"PeriodicalId\":15689,\"journal\":{\"name\":\"Journal of Engineering and Technological Sciences\",\"volume\":\" 60\",\"pages\":\"\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2023-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Engineering and Technological Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5614/j.eng.technol.sci.2023.55.5.8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering and Technological Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5614/j.eng.technol.sci.2023.55.5.8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband
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%.
期刊介绍:
Journal of Engineering and Technological Sciences welcomes full research articles in the area of Engineering Sciences from the following subject areas: Aerospace Engineering, Biotechnology, Chemical Engineering, Civil Engineering, Electrical Engineering, Engineering Physics, Environmental Engineering, Industrial Engineering, Information Engineering, Mechanical Engineering, Material Science and Engineering, Manufacturing Processes, Microelectronics, Mining Engineering, Petroleum Engineering, and other application of physical, biological, chemical and mathematical sciences in engineering. Authors are invited to submit articles that have not been published previously and are not under consideration elsewhere.