Research ArticleOpen AccessGoogle Scholar indexed
Online Finger Gesture Recognition Using Surface Electromyography Signals
School of Information Engineering, Southwest University of Science and Technology, Mianyang, China
School of Information Engineering, Southwest University of Science and Technology, Mianyang, China
- 1 School of Information Engineering, Southwest University of Science and Technology, Mianyang, China
- 2 School of Information Engineering, Southwest University of Science and Technology, Mianyang, China
Journal of Signal and Information Processing·Volume 04 (2013)·Pages 101–105·Published 8 May 2013·DOI10.4236/jsip.2013.42013
Copy link · social · email
Abstract
The analysis on the online finger gesture recognition using multi-channel sEMG signals was explored in this paper. Nine types of gestures were applied to be identified, involving six kinds of numerical finger gestures and three kinds of hand gestures. The time domain parameters were extracted to be the features. And then, the probabilistic neural network was utilized to classify the proposed gestures with the extracted features. The experimental results showed that most of gestures could acquire the acceptable classification performance and a few elaborate gestures were hard to acquire the effective identification.
KeywordsFinger GesturesEMG SignalOnlineRecognition
- M. A. Oskoei and H. Hu, “Myoelectric Control Systems—A Survey,” Biomedical Signal Processing and Control, Vol. 2, No. 4, 2007, pp. 275-294. doi:10.1016/j.bspc.2007.07.009
- X. Y. Li and A. S. Aruin, “Muscle Activity Onset Time Detection Using Teager-Kaiser Energy Operator,” 27th Annual Conference of IEEE Engineering in Medicine and Biology, Shanghai, 2005, pp. 7549-7552.
- J. U. Chu, I. Moon and M. S. Mun, “A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand,” IEEE Transactions on Biomedical Engineering, Vol. 53, No. 11, 2006, pp. 2232-2239. doi:10.1109/TBME.2006.883695
- M. Khezri and M. Jahed, “An Exploratory Study to Design a Novel Hand Movement Identification System,” Computers in Biology and Medicine, Vol. 39, No. 5, 2009, pp. 433-442. doi:10.1016/j.compbiomed.2009.02.001
- X. Chen, Q. Li, J. H. Yang and V. Lantz, “Test-Retest Repeatability of Surface Electromyography Measurement for Hand Gesture,” 2nd International Conference on Bioinformatics and Biomedical Engineering, Shanghai, China, 2008, pp. 1923-1926.
- J. Rafiee, M. A. Rafiee, F. Yavari and M. P. Schoen, “Feature Extraction of Forearm EMG Signals for Prosthetics,” Expert Systems with Applications, Vol. 38, No. 4, 2011, pp. 4058-4067. doi:10.1016/j.eswa.2010.09.068
- M. A. Oskoei and H. Hu, “Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb,” IEEE Transactions on Biomedical Engineering, Vol. 55, No. 8, 2008, pp. 1956-1965. doi:10.1109/TBME.2008.919734
- Y. C. Du, C. H. Lin, L. Y. Shyu and T. Chen, “Portable Hand Motion Classifier for Multi-Channel Surface Electromyography Recognition Using Grey Relational Analysis,” Expert Systems with Applications, Vol. 37, No. 6, 2010, pp. 4283-4291. doi:10.1016/j.eswa.2009.11.072
- L. Hargrove, K. Englehart and B. Hudgins, “A Training Strategy to Reduce Classification Degradation Due to Electrode Displacements in Pattern Recognition Based Myoelectric Control,” Biomedical Signal Processing and Control, Vol. 3, No. 2, 2008, pp. 175-180. doi:10.1016/j.bspc.2007.11.005
- W. H. Wang, X. Chen, P. Yang, Y. Li and J. H. Yang, “Chinese Sign Language Recognition Based on Multiple Sensors Information Detection and Fusion,” Chinese Journal of Biomedical Engineering, Vol. 29, No. 5, 2010, pp. 665-671.
- F. V. G. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. Etienne-Cummings and N. V. Thakor, “Decoding of Individuated Finger Movements Using Surface Electromyography,” IEEE Transactions on Biomedical Engineering, Vol. 56, No. 5, 2009, pp. 1427-1434. doi:10.1109/TBME.2008.2005485