Abstract
Bu
çalışmada EMG sinyalleri kullanılarak 14 farklı parmak hareketi önerilen yeni
bir öznitelik çıkarma yöntemiyle sınıflandırılmıştır. EMG sinyallerinden parmak
hareketlerinin tespiti / sınıflandırılması, ön işleme, özellik çıkarma ve
sınıflandırma adımlarını içeren 3 ana adımdan oluşur. EMG sinyallerinin
sınıflandırılmasında, sınıflandırıcının performansı doğrudan öznitelik çıkarma
yöntemine bağlıdır. Bu sebeple öznitelik çıkarma yöntemi uygun bir biçimde
seçilmelidir. Literatürde zaman, histogram ve frekans tabanlı birçok öznitelik
çıkarma yöntemi vardır. Ancak bu yöntemler yüksek zaman karmaşıklığı, çok fazla
işleme ihtiyaç duyma, fazla sayıda kontrol parametresi bulundurma, vb. birçok
dezavantaja sahiptir. Bu çalışmada EMG sinyallerinden çeşitli parmak
hareketlerinin sınıflandırılması amacıyla sayılan dezavantajları ortadan
kaldırmak için yeni bir öznitelik çıkarma yöntemi sunulmuştur. Bu yöntem iki
zaman tabanlı öznitelik çıkarma yönteminin hibritleştirilmesiyle elde
edilmiştir. 10 kat çapraz doğrulamayla elde edilen deneysel sonuçlarına göre, önerilen
yöntemin %97,48 doğruluk oranıyla bu çalışmada kullanılan diğer 9 öznitelik
çıkarma yöntemlerinden daha iyi olduğu deneysel olarak gösterilmiştir. Deneysel
sonuçlar istatistiksel yöntemlerle desteklenmiştir.
References
- ZHANG, X., CHEN, X., LI, Y., LANTZ, V., WANG, K., YANG, J., “A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41, 1064-1076, 2011
- [SAPSANIS, C., GEORGOULAS, G., TZES, A. “EMG Based Classification of Basic Hand Movements Based on Time-Frequency Features”, 21. Mediterranean Conference on Control and Automation, 716-722, 2013.
- [BAŞTÜRK, A., YÜKSEL, M. E., CALISKAN, A., BADEM, H., “Deep Neural Network Classifier for Hand Movement Prediction”, 25. Signal Processing and Communications Applications Conference (SIU), 1-4.,2017
- TENORE, F.V., RAMOS, A., FAHMY, A., ACHARYA, S., ETIENNE-CUMMINGS, R., THAKOR, N. V., “Decoding of Individuated Finger Movements Using Surface Electromyography”, IEEE Transactions on Biomedical Engineering”, 56, 1427-1434, 2009.
- KHUSHABA, R. N., KODAGODA, S., LİU, D., DİSSANAYAKE, G., “Muscle Computer Interfaces for Driver Distraction Reduction”. Computer Methods and Programs in Biomedicine”, 110, 137-149, 2013.
- PHINYOMARK, A., N KHUSHABA, R., SCHEME, E., “Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors”, Sensors, 18, 1615-1632, 2018.
- CHAN, F. H., YANG, Y. S., LAM, F. K., ZHANG, Y. T., PARKER, P. A., “Fuzzy EMG Classification for Prosthesis Control”, IEEE Transactions on Rehabilitation Engineering, 8, 305-311, 2000.
- REAZ, M. B. I., HUSSAIN, M. S., MOHD-YASIN, F., “Techniques of EMG Signal Analysis: Detection, Processing, Classification And Applications”, Biological Procedures Online, 8, 11, 2006
- PHİNYOMARK, A., PHUKPATTARANONT, P., LIMSAKUL, C., “Feature Reduction and Selection for EMG Signal Classification”, Expert Systems with Applications,39, 7420-7431, 2012
- BAŞTÜRK, A.., BAŞTÜRK, N. S., QURBANOV, O. , “A Comparative Performance Analysis Of Various Classifiers For Fingerprint Recognition”, Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi,, 7(2), 504-513, 2018.
- CALISKAN A.,, BADEM H., BASTURK A., YUKSEL M. E., “Diagnosis Of The Parkinson Disease By Using Deep Neural Network Classifier.” Istanbul University-Journal of Electrical & Electronics Engineering, 17(2), 3311-3319, 2017.
- https://www.rami-khushaba.com/electromyogram-emg-repository.html (erişim tarihi 21.03.2019)
- COVER T. HART P., “Nearest Neighbor Pattern Classification.,” IEEE Transactions On Information Theory, 13(1), 21-27, 1967.
- HOSSIN M., SULAIMAN N., “A Review On Evaluation Metrics For Data Classification Evaluations.,” International Journal of Data Mining & Knowledge Management Process,, 5(2), 2015.
Abstract
This study
aims to discuss classification of 14 different finger movements from EMG signals
by using new feature extraction technique. The detection/classification of finger movements consists of 3 main
steps including, preprocessing, feature extraction and classification steps. In
classification of EMG signals, the performance of the classifier directly depends
on feature extraction methods, including, time, histogram and frequency-based
methods. However, these feature extraction methods have several drawbacks
including, high time complexity, high computation demand, user supplied
parameters, etc. In this paper, a new feature extraction method has been
proposed for the classification of finger movements from EMG signals to
overcome these problems. The proposed method based on hybridization of 2-time
domain feature extraction techniques. The use of this method resulted in an accuracy
of 97.48% after 10-fold-cross-validation. The experimental results supported
with statistical analysis show that proposed method is better than 9 feature
extraction methods investigated in this paper
References
- ZHANG, X., CHEN, X., LI, Y., LANTZ, V., WANG, K., YANG, J., “A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41, 1064-1076, 2011
- [SAPSANIS, C., GEORGOULAS, G., TZES, A. “EMG Based Classification of Basic Hand Movements Based on Time-Frequency Features”, 21. Mediterranean Conference on Control and Automation, 716-722, 2013.
- [BAŞTÜRK, A., YÜKSEL, M. E., CALISKAN, A., BADEM, H., “Deep Neural Network Classifier for Hand Movement Prediction”, 25. Signal Processing and Communications Applications Conference (SIU), 1-4.,2017
- TENORE, F.V., RAMOS, A., FAHMY, A., ACHARYA, S., ETIENNE-CUMMINGS, R., THAKOR, N. V., “Decoding of Individuated Finger Movements Using Surface Electromyography”, IEEE Transactions on Biomedical Engineering”, 56, 1427-1434, 2009.
- KHUSHABA, R. N., KODAGODA, S., LİU, D., DİSSANAYAKE, G., “Muscle Computer Interfaces for Driver Distraction Reduction”. Computer Methods and Programs in Biomedicine”, 110, 137-149, 2013.
- PHINYOMARK, A., N KHUSHABA, R., SCHEME, E., “Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors”, Sensors, 18, 1615-1632, 2018.
- CHAN, F. H., YANG, Y. S., LAM, F. K., ZHANG, Y. T., PARKER, P. A., “Fuzzy EMG Classification for Prosthesis Control”, IEEE Transactions on Rehabilitation Engineering, 8, 305-311, 2000.
- REAZ, M. B. I., HUSSAIN, M. S., MOHD-YASIN, F., “Techniques of EMG Signal Analysis: Detection, Processing, Classification And Applications”, Biological Procedures Online, 8, 11, 2006
- PHİNYOMARK, A., PHUKPATTARANONT, P., LIMSAKUL, C., “Feature Reduction and Selection for EMG Signal Classification”, Expert Systems with Applications,39, 7420-7431, 2012
- BAŞTÜRK, A.., BAŞTÜRK, N. S., QURBANOV, O. , “A Comparative Performance Analysis Of Various Classifiers For Fingerprint Recognition”, Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi,, 7(2), 504-513, 2018.
- CALISKAN A.,, BADEM H., BASTURK A., YUKSEL M. E., “Diagnosis Of The Parkinson Disease By Using Deep Neural Network Classifier.” Istanbul University-Journal of Electrical & Electronics Engineering, 17(2), 3311-3319, 2017.
- https://www.rami-khushaba.com/electromyogram-emg-repository.html (erişim tarihi 21.03.2019)
- COVER T. HART P., “Nearest Neighbor Pattern Classification.,” IEEE Transactions On Information Theory, 13(1), 21-27, 1967.
- HOSSIN M., SULAIMAN N., “A Review On Evaluation Metrics For Data Classification Evaluations.,” International Journal of Data Mining & Knowledge Management Process,, 5(2), 2015.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Computer Engineering |
| Authors | |
| Publication Date | July 31, 2019 |
| Submission Date | March 21, 2019 |
| Acceptance Date | May 17, 2019 |
| Published in Issue | Year 2019 |
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