EMG Controlled Wheelchair Movement based on Masseter and Buccinators Muscles

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2016 by IJETT Journal
Volume-37 Number-3
Year of Publication : 2016
Authors : Hayder A. Azeez, Norasmadi Abdul Rahim, Muhammad Juhairi Aziz bin safar
DOI :  10.14445/22315381/IJETT-V37P221


Hayder A. Azeez, Norasmadi Abdul Rahim, Muhammad Juhairi Aziz bin safar"EMG Controlled Wheelchair Movement based on Masseter and Buccinators Muscles", International Journal of Engineering Trends and Technology (IJETT), V37(3),133-139 July 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

There are many elderly people who lack the control of movement of their upper or lower limbs. There are also many people affected and who are paralyzed. For this reason, the wheelchair is very important for these people to help them daily. The aim of this work is to present a control method of wheelchair movement in five directions: (forward, reverse direction, turn left, turn right, and stop condition) based on the signals of electromyography (EMG), where the EMG signals are collected from the masseter and buccinators muscles, and then extracted features of the autoregressive model (AR model) 4-order, and classify it by using the Knearest neighbor classifier (KNN) and use it as a control signal for the wheelchair’s movement. The rate of classification was a maximum when the value of K=1 and K=2, where the accuracy of the proposed method was as the following: double blowing 98.1, double clenching 97.3, single blowing 96.5, and single clenching 98.1.


[1] M. Nihei and M.G. Fujie, Proposal for a New Development Methodology for Assistive Technology Based on a Psychological Model of Elderly People. INTECH Open Access Publisher 2012.
[2] A. De Santis and D .Iacoviello, ?Robust real time eye tracking for computer interface for disabled people, computer methods and programs in biomedicine, vol. 96, pp.1-11, 2009.
[3] L. Wei, and H. Hu, ?A hybrid human-machine interface for hands-free control of an intelligent wheelchair, International journal of mechatronics and automation, vol. 1, pp.97-111, 2011.
[4] F. Galán, M. Nuttin., E. Lew, P.W. Ferrez, G. Vanacker, J. Philips, and J.D.R Millán, A brain-actuated wheelchair: asynchronous and non-invasive brain–computer interfaces for continuous control of robots. Clinical Neurophysiology, vol. 119, pp.2159-2169, 2008.
[5] Yi, Zhang, Dai Lingling, Luo Yuan, and Hu. Huosheng "Design of a surface EMG based human-machine interface for an intelligent wheelchair." InElectronic Measurement & Instruments (ICEMI), 2011 10th International Conference on, vol. 3, pp. 132-136.
[6] R. Maskeliunas, and R. Simutis, ?Multimodal wheelchair control for the paralyzed people, Elektronika ir Elektrotechnika (Electronics and Electrical Engineering), vol. 5, pp.81-84, 2011.
[7] P. Jia, H.H. Hu, T. Lu, and K. Yuan, ?Head gesture recognition for hands-free control of an intelligent wheelchair. Industrial Robot: An International Journal, vol. 34, pp.60-68, Jan, 2007.
[8] G. Pires, and U. Nunes, ?A wheelchair steered through voice commands and assisted by a reactive fuzzy-logic controller. Journal of Intelligent and Robotic Systems, vol. 34, pp.301-314, March, 2002.
[9] I. Moon, L. Myungjoon, C. Junuk, and M. Museong, "Wearable EMG-based HCI for electric-powered wheelchair users with motor disabilities." In proceedings of the 2005 IEEE international conference on robotics and automation, pp. 2649-2654.
[10] L. Wei, Hu. Huosheng, and Y. Kui, "Use of forehead biosignals for controlling an intelligent wheelchair." In Robotics and Biomimetics, 2008. ROBIO 2008. IEEE International Conference on, pp. 108-113.
[11] H. Ohkubo, and T. Shimono, ?Motion control of mobile robot by using myoelectric signals based on functionally different effective muscle theory. In Mechatronics (ICM), 2013 IEEE International Conference on, pp. 786-791.
[12] A. Phinyomark, C. Limsakul, and P. Phukpattaranont, ?A novel feature extraction for robust EMG pattern recognition, 2009.
[13] M.B.I. Reaz, M.S. Hussain, and F. Mohd-Yasin, ?Techniques of EMG signal analysis: detection, processing, classification and applications, Biological procedures online, pp.11-35, 2006.
[14] C.J. De Luca, Surface electromyography: Detection and recording.DelSys Incorporated, 10, p.2011, 2002.
[15] S.D. Kreibig, F.H. Wilhelm, W.T. Roth, and J.J. Gross, ?Affective modulation of the acoustic startle: Does sadness engage the defensive system, Biological psychology, VOL. 87, pp.161-163, 2011.
[16] Z. HORVATH, and R. JOHNSTON, ?GARCH TIME SERIES PROCESS Econometrics, 7590 Projects 2 and 3. [17] X. Xu, Y. Zhang, Y. Luo, and D. Chen, ?Robust bio-signal based control of an intelligent wheelchair. Robotics, VOL. 2, pp.187-197, 2013.
[18] M.Z. HH Al-Faiz, A.A. Ali, and A.H. Miry, ?A k-nearest neighbor based algorithm for human arm movements recognition using EMG signals, In Energy, Power and Control (EPC-IQ), 2010 1st International Conference on, pp. 159-167.
[19] C. Lee, S.K. Yoo, Y. Park, N. Kim, K. Jeong, and B. Lee, ?Using neural network to recognize human emotions from heart rate variability and skin resistance, In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 5523-5525.
[20] M. Murugappan, ?Electromyogram signal based human emotion classification using KNN and LDA, In System Engineering and Technology (ICSET), 2011 IEEE International Conference on, pp. 106-110.

Intelligent Wheelchair, EMG, AR model, K-nearest neighbour classifier.