Isi Artikel Utama

Zehra Karapinar Senturk
Duzce University
Melahat Sevgul Bakay
Duzce University
Vol. 10 No. 2 (2021), Articles
How to Cite


Electromyography (EMG) data gives information about the electrical activity related to muscles. EMG data obtained from arm through sensors helps to understand hand gestures. For this work, hand gesture data were taken from UCI2019 EMG dataset obtained from MYO thalmic armband were classied with six dierent machine learning algorithms. Articial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF) methods were preferred for comparison based on several performance metrics which are accuracy, precision, sensitivity, specicity, classication error, kappa, root mean squared error (RMSE) and correlation. The data belongs to seven hand gestures. 700 samples from 7 classes (100 samples per group) were used in the experiments. The splitting ratio in the classication was 0.8-0.2, i.e. 80% of the samples were used in training and 20% of data were used in testing phase of the classier. NB was found to be the best among other methods because of high accuracy (96.43%) and sensitivity (96.43%) and the lowest RMSE (0.189). Considering the results of the performance parameters, it can be said that this study recognizes and classies seven hand gestures successfully in comparison with the literature.


Download data is not yet available.

Rincian Artikel


Acharya, U. R., Dua, S., Du, X., Sree S, V., and Chua, C. K., (2011). Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Transactions on Information Technology in Biomedicine, 15(3):449–455. ISSN 10897771. doi:10.1109/TITB.2011.2119322.

Bian, F., Li, R., and Liang, P., (2017). SVM based simultaneous hand movements classification using sEMG signals. In 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017, pages 427–432. Institute of Electrical and Electronics Engineers Inc. ISBN 9781509067572. doi:10.1109/ICMA. 2017.8015855.

Donovan, I., Valenzuela, K., Ortiz, A., Dusheyko, S., Jiang, H., Okada, K., and Zhang, X., (2017). MyoHMI: A low-cost and flexible platform for developing real-time human machine interface for myoelectric controlled applications. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, pages 4495–4500. Institute of Electrical and Electronics Engineers Inc. ISBN 9781509018970. doi:10.1109/SMC.2016.7844940.

Englehart, K., Hudgins, B., and Parker, P. A., (2001). A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 48(3):302–311. ISSN 00189294. doi:10.1109/10.914793.

Eshitha, K. V. and Jose, S., (2018). Hand Gesture Recognition Using Artificial Neural Network. In 2018 International Conference on Circuits and Systems in Digital Enterprise Technology, ICCSDET 2018. Institute of Electrical and Electronics Engineers Inc. ISBN 9781538605769. doi:10.1109/ICCSDET.2018.8821076.

Faust, O. and Bairy, M. G., (2012). Nonlinear analysis of physiological signals: A review. Journal of Mechanics in Medicine and Biology, 12(4). ISSN 02195194. doi:10.1142/S0219519412400155.

Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., and Acharya, U. R., (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161:1–13. ISSN 18727565. doi:10.1016/j.cmpb.2018.04.005.

Guo, W., Sheng, X., Liu, H., and Zhu, X., (2017). Toward an Enhanced Human-Machine Interface for Upper-Limb Prosthesis Control with Combined EMG and NIRS Signals. IEEE Transactions on Human-Machine Systems, 47(4):564–575. ISSN 21682291. doi:10.1109/THMS.2016.2641389.

Jayalakshmi, T. and Santhakumaran, A., (2011). Statistical Normalization and Back Propagationfor Classification. International Journal of Computer Theory and Engineering, 3(1):89–93. ISSN 17938201. doi:10.7763/ ijcte.2011.v3.288.

Jiang, S., Gao, Q., Liu, H., and Shull, P. B., (2020). A novel, co-located EMG-FMG-sensing wearable armband for hand gesture recognition. Sensors and Actuators, A: Physical, 301. ISSN 09244247. doi:10.1016/j.sna. 2019.111738.

Kim, J. H., Hong, G. S., Kim, B. G., and Dogra, D. P., (2018). deepGesture: Deep learning-based gesture recognition scheme using motion sensors. Displays, 55:38–45. ISSN 01419382. doi:10.1016/j.displa.2018. 08.001.

Liang, H., Yuan, J., Thalmann, D., and Nadia, M. T., (2015). AR in hand: Egocentric palm pose tracking and gesture recognition for augmented reality applications. In MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, pages 743–744. Association for Computing Machinery, Inc. ISBN 9781450334594. doi:10.1145/2733373.2807972.

Mi, J., Sun, Y., Wang, Y., Deng, Z., Li, L., Zhang, J., and Xie, G., (2016). Gesture recognition based teleoperation framework of robotic fish. In 2016 IEEE International Conference on Robotics and Biomimetics, ROBIO 2016, pages 137–142. Institute of Electrical and Electronics Engineers Inc. ISBN 9781509043644. doi: 10.1109/ROBIO.2016.7866311.

Molchanov, P., Gupta, S., Kim, K., and Pulli, K., (2015). Multi-sensor system for driver’s hand-gesture recognition. In 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015. Institute of Electrical and Electronics Engineers Inc. ISBN 9781479960262. doi:10.1109/FG. 2015.7163132.

Sagayam, K. M. and Hemanth, D. J., (2017). Hand posture and gesture recognition techniques for virtual reality applications: a survey. Virtual Reality, 21(2):91–107. ISSN 14349957. doi:10.1007/s10055-016-0301-0.

Simão, M., Neto, P., and Gibaru, O., (2019a). EMG-based online classification of gestures with recurrent neural networks. Pattern Recognition Letters, 128:45–51. ISSN 01678655. doi:10.1016/j.patrec.2019.07.021.

Simão, M., Neto, P., and Gibaru, O., (2019b). Improving novelty detection with generative adversarial networks on hand gesture data. Neurocomputing, 358:437–445. ISSN 18728286. doi:10.1016/j.neucom.2019.05.064.

Singh, D. and Singh, B., (2019). Investigating the impact of data normalization on classification performance. Applied Soft Computing Journal. ISSN 15684946. doi:10.1016/j.asoc.2019.105524.

Vaiman, M., (2007). Standardization of surface electromyography utilized to evaluate patients with dysphagia. Head and Face Medicine, 3(1). ISSN 1746160X. doi:10.1186/1746-160X-3-26.

Van Drongelen, W., (2018). Signal Processing for Neuroscientists. Academic Press, 2 edition. ISBN 9780128104835.

Wahid, M. F., Tafreshi, R., Al-Sowaidi, M., and Langari, R., 2018. Subject-independent hand gesture recognition using normalization and machine learning algorithms. Journal of Computational Science, 27:69–76. ISSN 18777503. doi:10.1016/j.jocs.2018.04.019.

Wei, W., Wong, Y., Du, Y., Hu, Y., Kankanhalli, M., and Geng, W., (2019). A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface. Pattern Recognition Letters, 119:131–138. ISSN 01678655. doi:10.1016/j.patrec.2017.12.005.

Xie, B., Li, B., and Harland, A., (2018). Movement and Gesture Recognition Using Deep Learning and Wearable-sensor Technology. In ACM International Conference Proceeding Series, pages 26–31. Association for Computing Machinery. ISBN 9781450365246. doi:10.1145/3268866.3268890.

Zhang, X., Chen, X., Li, Y., Lantz, V., Wang, K., and Yang, J., (2011). 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(6):1064–1076. doi:10.1109/TSMCA.2011.2116004.