Open AccessInvestigation of the Number of Features and Muscles for an Effective Hand Motion Classifier Using Electromyography Signal
Author(s) -
Triwiyanto Triwiyanto,
Bedjo Utomo,
Dyah Titisari,
Muhammad Ridha Mak’ruf,
Triana Rahmawati
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1373/1/012051
Subject(s) - electromyography , linear discriminant analysis , pattern recognition (psychology) , support vector machine , artificial intelligence , feature vector , computer science , standard deviation , waveform , feature (linguistics) , signal (programming language) , decision tree , random forest , speech recognition , time domain , root mean square , mathematics , motion (physics) , computer vision , statistics , physical medicine and rehabilitation , engineering , medicine , telecommunications , radar , linguistics , philosophy , electrical engineering , programming language
The essential problem in the development of a prosthetic hand based on electromyography (EMG) signal is the choice of the features and number of muscles that will be used to recognize the hand motion. A minimal number of feature and channel could reduce the cost in the development of the prosthetic hand and processing time. Therefore, it is important to obtain the correct number of feature and muscles in the system. The objective of this study is to evaluate the effectiveness of using the number of feature and muscle to recognize the hand motion for amputee person using the EMG signal. In this study, the EMG dataset was obtained from five transradial amputee persons. Ten disposable electrodes (Ag/AgCl) were placed on the residual hand with equal space between the electrodes. In order to obtain the EMG features which are related to the hand motion, each of the EMG signal was extracted using six-time domain features which are root mean square (RMS), integrated EMG (IEMG), waveform length (WL), difference absolute standard deviation value (DASDV), Wilson amplitude, and myopulse rate (MYOP). Each feature was evaluated using Decision Tree, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-nearest neighborhood, and Ensemble machine learning (ML). The number of class to be classified in this study was eighteen motions. The effectiveness of using the number of features and muscles was evaluated by varying the number of features and muscles. Further, the accuracy, in the discrimination of the motion, was calculated and compared among the machine learnings. The results of this study show that six muscles can effectively classify the eighteen of hand motion. The ML with WL feature has the highest accuracy among the others (81.3% based on quadratic SVM). The study suggested the effective number of muscle and feature which can be used in the prosthetics hand development so that the effective prosthetic machine can be built with a low cost in the time computing and the hardware for data acquisition.