Abstract
Normal hand gesture recognition methods using surface Electromyography (sEMG) signals require designers to use digital signal processing hardware or ensemble methods as tools to solve real time hand gesture classification. These ways are easy to result in complicated computation models, inconvenience of circuit connection and lower online recognition rate. Therefore it is imperative to have good methods which can avoid the problems mentioned above as more as possible. An online hand gesture recognition model by using Flexible Neural Trees (FNT) and based on sEMG signals is proposed in this paper. The sEMG is easy to record electrical activity of superficial muscles from the skin surface which has applied in many fields of treatment and rehabilitation. The FNT model can be created using the existing or modified tree- structure- based approaches and the parameters are optimized by the PSO algorithm. The results indicate that the model is able to classify six different hand gestures up to 97.46% accuracy in real time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chen, Y., Abraham, A.: Flexible Neural Trees: Theoretical Foundations. In: Perspectives and Applications, June 27, Springer, Heidelberg (2009)
Lichtenauer, J.F., Hendriks, E.A., Reinders, M.J.T.: Sign Language Recognition by Combining Statistical DTW and Independent Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(11), 2040–2046 (2008)
Phinyomark, A., Limsakul, C., Phukpattaranont, P.: A Novel Feature Extraction for Robust EMG Pattern Recognition. Journal of Computing 1(1), 71–80 (2009)
Chen, Y., Abraham, A., Yang, B.: Feature selection and classification using flexible neural tree. Neurocomputing 70, 305–313 (2006)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceeding of 1995 IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE, New York (1995)
Khezri, M., Jahed, M.: Real-time intelligent pattern recognition algorithm for surface EMG signals. BioMedical Engineering OnLine (December 2007)
Arjunan, S.P., Kumar, D.K., Naik, G.R., Guo, Y., Shimada, H.: A framework towards real time control of virtual robotic hand: Interface based on low-level forearm muscle movements. In: IEEE International Conference on Intelligent Human Computer Interaction, Allahabad, India, January 16-18 (2010)
Guo, Y., Li, Y.: Wireless Surface Electromyography Sensor Network Based on Rapid Prototyping. Sensor Letters 9(5) (2011) (accepted September 5, 2010)
Dong, Z.S., Guo, Y., Zeng, J.C.: Recurrent Hidden Markov Models Using Particle Swarm Optimization. Int. J. Modelling, Identification and Control (November 2010) (accepted)
Guo, Y., Li, Y.: Single Channel Electromyography Blind Recognition System of 3D Hand. Computer Applications and Software (September 2010)
Yina, G.: Research of Hand Gesture Identification of sEMG based on SCICA. In: IEEE The 2nd International Conference on Signal Processing Systems (ICSPS 2010), Dalian, China (July 2010) [EI 20104013270588]
Naik, G.R., Kumar, D.K., Arjunan, S.: Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques. Measurement Science Review 10(1), 1–6 (2010)
Naik, G.R., Kumar, D.K.: Inter-experimental discrepancy in Facial Muscle activity during Vowel utterance. In: Computer Methods in Biomechanics and Biomedical Engineering. Taylor and Francis, Abington (2009) (accepted), SCI Impact factor 1.301
Naik, G.R., Kumar, D.K., Jayadeva: Twin SVM for Gesture Classification using the Surface Electromyogram. IEEE Transactions on Information Technology in BioMedicine (November 2009) (accepted). SCI Impact factor 1.94
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Q., Guo, Y., Abraham, A. (2011). Online Hand Gesture Recognition Using Surface Electromyography Based on Flexible Neural Trees. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-23896-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
Online ISBN: 978-3-642-23896-3
eBook Packages: Computer ScienceComputer Science (R0)