Design of Real-Time Document Control Based on Zigbee and Surface Electromyography (sEMG)

Zhen Wang, Bei Wang, Xingyu Wang

Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China Key Laboratory of Advanced Control and Optimization for Chemical Processes,(East China University of Science and Technology), Ministry of Education, Shanghai, 200237, China.

DOI: 10.4236/eng.2013.510B036                

Abstract

The human-computer interaction (HCI) is now playing a great role in computer technology. This study introduces an automatic document control technique which is based on the human hand waving movements. The recognition of hand movement is realized according to the surface electromyography (sEMG). A collector is set on the forearm. The sEMG signal is recorded and conveyed to a PC terminal by using wireless Zigbee. An automatic algorithm is developed in order to extract the characteristics of sEMG, recognize the waving movements, and transmit to document control command. The developed human-computer interaction technique can be used as a new gallery for teaching, as well as an assistant tool for disabled person.

Keywords

Surface Electromyography; Human-Computer Interaction; Zigbee; Document Control

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	K. Ando, K. Nagata, D. Kitagawa, N. Shibata, M. Ya- mada and K. Magatani, “Development of the Input Equipment for a Computer Using Surface EMG,” 28th Annual International Conference of the IEEE Engineering in Medicine and Biology, New York City, 2006, pp. 1331-1334.
[2]	X. Chen and Z. Jane Wang, “Pattern Recognition of Number Gestures Based on a Wireless Surface EMG System,” Biomedical Signal Processing and Control, Vol. 8, 2013, pp. 184-192. http://dx.doi.org/10.1016/j.bspc.2012.08.005
[3]	G. R Naik, D. K Kumar, S. P Arjunan, H. Weghorn and M. Palaniswami, “Limitations and Applications of ICA in Facial sEMG and Hand Gesture sEMG for Human Computer Interaction. Digital Image Computing Techniques and Applications,” 9th Biennial Conference of the Australian Pattern Recognition Society, Vol. 58, 2007, pp. 15-22.
[4]	L. Vigon, M. R. Saatchi, J. E. W. Mayhew and R. Fernandes, “Quantitative Evaluation of Techniques for Ocular Artefact Filtering of EEG Waveforms,” IEE Proceedings of Science, Measurement and Technology, Vol. 147, No. 5, 2000, pp. 219-228. http://dx.doi.org/10.1049/ip-smt:20000475
[5]	T. W. Lee, “Independent Component Analysis, Theory and Application,” Kluwer Academic Publishers, Boston, 1998.
[6]	N. W. Willigenburg, A. Daffertshofer, I. Kingma and J. H. Dieen, “Removing ECG Contamination from EMG Recordings: A Comparison of ICA-Based and Other Filtering Procedures,” Journal of Electromyography and Kine-siology, Vol. 22, 2012, pp. 485-493. http://dx.doi.org/10.1016/j.jelekin.2012.01.001
[7]	K. Kiguchi and Y. Hayashi, “An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot,” IEEE Transactions on System, Man, and Cybernetics-Part B: Cybernetics, Vol. 42, No. 4, 2012, pp. 1064-1071.
[8]	J. U. Chu, I. Monn and M. S. Mun, “A Real-Time EMG Pattern Recognization 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. http://dx.doi.org/10.1109/TBME.2006.883695
[9]	Y. Yu, C. Xiang, T. Youqiang, Z. Xu and Y. jihai, “Human-Machine Interaction System Based on Surface EMG Signals,” Journal of System Simulation, Vol. 22, No. 3, 2010, pp. 651-655.