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Electromyography (EMG) signal classification for wrist movement using naïve bayes classifier
Electromyography (EMG) signal classification for wrist movement using naïve bayes classifier
- Source :
- Journal of Physics: Conference Series. 1424:012013
- Publication Year :
- 2019
- Publisher :
- IOP Publishing, 2019.
-
Abstract
- Electromyography (EMG) signal is an myoelectric signal in the muscle layer. It occurs caused by contraction and relaxation muscle activity. This article provide numerical study of the classifying the electromyography signal for wrist movement combined with open and grasping finger flexor. The EMG signal has recorded using a device called electromyography. It has acquired by attaching an surface electrode in the skin then the electrode was capturing the raw signal. The volunteer involved were six where each volunteer has ten datasets the EMG signal. The surface electrode are sticked in the lower arm muscle. The EMG raw signal was processed using zero-mean normalization. The feature extraction method is root mean square (rms), mean absolute value (mav), variance (var), and standard deviation (std). This EMG signal has been classified by naïve bayes classifier. Training and testing data was using 5-cross validation. The result indicates that the classification accuracy for classifying the EMG signal for wrist movement combined open finger flexor (OFF) and grasping finger flexor (GFF) is 70% and 75% respectively. Therefore, the EMG signal can be applied for identificating of muscle disorder, prostheses hand and biometric system.
- Subjects :
- History
medicine.diagnostic_test
business.industry
Movement (music)
Computer science
Pattern recognition
Electromyography
Wrist
musculoskeletal system
Computer Science Applications
Education
body regions
Naive Bayes classifier
medicine.anatomical_structure
Signal classification
medicine
Artificial intelligence
business
Subjects
Details
- ISSN :
- 17426596 and 17426588
- Volume :
- 1424
- Database :
- OpenAIRE
- Journal :
- Journal of Physics: Conference Series
- Accession number :
- edsair.doi...........2f2fa9be967e1fec35528b52ba6d6a84
- Full Text :
- https://doi.org/10.1088/1742-6596/1424/1/012013