In this paper, an adaptive human-machine interaction (HMI) method that is based on surface electromyography (sEMG) signals is proposed for the hands-free control of an intelligent wheelchair. sEMG signals generated by the facial movements are obtained by a convenient dry electrodes sensing device. After the signals features are extracted from the autoregressive model, control data samples are updated and trained by an incremental online learning algorithm in real-time. Experimental results show that the proposed method can significantly improve the classification accuracy and training speed. Moreover, this method can effectively reduce the influence of muscle fatigue during a long time operation of sEMG-based HMI.
References
[1]
Nihei, M.; Fujie, M.G. Proposal for a New Development Methodology for Assistive Technology Based on a Psychological Model of Elderly People. In Assistive Technologies; InTech: New York, NY, USA, 2012.
[2]
Pires, G.; Nunes, U. A wheelchair steered through voice commands and assisted by a reactive fuzzy-logic controller. J. Intell. Rob. Syst.？2002, 34, 301–314, doi:10.1023/A:1016363605613.
[3]
De Santis, A.; Iacoviello, D. Robust real time eye tracking for computer interface for disabled people. Comput. Methods Progr. Biol.？2009, 96, 1–11, doi:10.1016/j.cmpb.2009.03.010.
[4]
Jia, P.; Hu, H.; Lu, T.; Yuan, K. Head gesture recognition for hands-free control of an intelligent wheelchair. Int. J. Ind. Rob.？2007, 34, 60–68, doi:10.1108/01439910710718469.
[5]
Wei, L.; Hu, H. A hybrid human-machine interface for hands-free control of an intelligent wheelchair. Int. J. Mechatron. Autom.？2011, 1, 97–111, doi:10.1504/IJMA.2011.040040.
[6]
Galan, F.; Nuttin, M.; Lew, E.; Ferrez, P.W.; Vanacker, G.; Philips, J.; Millan, J.R. A brain-actuated wheelchair: Asynchronous and non-invasive Brain-computer interfaces for continuous control of robots. Clin. Neurophysiol.？2008, 119, 2159–2169.
[7]
Rebsamen, B.; Guan, C.; Zhang, H.; Wang, C.; Teo, C.; Ang, M.J.; Burdet, E. A brain controlled wheelchair to navigate in familiar environments. IEEE Trans. Neural Syst. Rehabil. Eng.？2010, 18, 590–598, doi:10.1109/TNSRE.2010.2049862.
[8]
Maskeliunas, R.; Simutis, R. Multimodal wheelchair control for the paralyzed people. Electron. Electr. Eng.？2011, 111, 81–84.
[9]
Hashimoto, M.; Takahashi, K.; Shimada, M. Wheelchair Control Using an EOG-and EMG-Based Gesture Interface. In Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore, 14–17 July 2009; pp. 1212–1217.
[10]
Santana, A.; Yang, C. Robotic control using physiological EMG and EEG signals. Adv. Auton. Rob.？7429, 449–450.
[11]
Brando, A.S.; Felix, L.B.; Bastos-Filho, T.F.; Sarcinelli-Filho, M. Controlling devices using biological signals. J. Adv. Rob. Syst.？2011, 8, 22–33.
[12]
Englehart, K.; Hudgins, B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng.？2003, 50, 848–854, doi:10.1109/TBME.2003.813539.
[13]
Moon, I.; Lee, M.; Chu, J.; Mun, M. Wearable EMG-Based HCI for Electric-Powered Wheelchair Users with Motor Disabilities. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), Barcelona, Spain, 18–22 April 2005; pp. 2649–2654.
[14]
Gandevia, S.C. Spinal and supraspinal factors in human muscle fatigue. Physiol. Rev.？2001, 81, 1725–1789.
[15]
Tamura, H.; Murata, T.; Yamashita, Y.; Tanno, K.; Fuse, Y. Development of the electric wheelchair hands-free semi-automatic control system using the surface-electromyogram of facial muscles. Artif. Life Rob.？2012, 17, 300–305, doi:10.1007/s10015-012-0060-2.
[16]
Zhang, Q.; Hayashibe, M.; Fraisse, P.; Guiraud, D. FES-induced torque prediction with evoked EMG sensing for muscle fatigue tracking. ASME Trans. Mechatron.？2011, 16, 816–826, doi:10.1109/TMECH.2011.2160809.
[17]
V？llestad, N.K. Measurement of human muscle fatigue. J. Neurosci. Methods？1997, 74, 219–227, doi:10.1016/S0165-0270(97)02251-6.
[18]
Williams, C. Human Muscle Fatigue; Routledge: New York, NY, USA, 2009.
[19]
Oskoei, M.; Hu, H. Myoelectric control systems: A survey. J. Biomed. Signal Process. Control？2007, 2, 275–294, doi:10.1016/j.bspc.2007.07.009.
[20]
Nikooyan, A.A.; Zadpoor, A.A. Effects of muscle fatigue on the ground reaction force and soft-tissue vibrations during running: A model study. IEEE Trans. Biomed. Eng.？2012, 59, 797–804, doi:10.1109/TBME.2011.2179803.
[21]
Alty, S.R.; Georgakis, A. Mean Frequency Estimation of Surface EMG Signals Using Filterbank Methods. In In Proceedings of the 19th European Signal Processing Conference (EUSIPCO 2011), Barcelona, Spain, 29 August–2 September 2011.
[22]
Gerdle, B.; Elert, J. The temporal occurrence of the mean power frequency shift of the electromyogram during maximum prolonged dynamic and static working cycles. Int. J. Sports Med.？1994, 15, 32–37, doi:10.1055/s-2007-1021107.
[23]
Lucas, M.F.; Gaufriau, A.; Pascual, S.; Doncarli, C.; Farina, D. Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization. J. Biomed. Signal Process. Control？2008, 3, 169–174, doi:10.1016/j.bspc.2007.09.002.
[24]
Kawano, S.; Okumura, D.; Tamura, H.; Tanaka, H.; Tanno, K. Online learning method using support vector machine for surface-electromyogram recognition. J. Artif. Life Rob.？2009, 13, 483–487, doi:10.1007/s10015-008-0607-4.
[25]
Vapnik, V. The Nature of Statistical Learning Theory; Springer: Heidelberg, Germany, 1999.
[26]
Chang, C.C.; Lin, C.J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol.？2011, 2, 27:1–27:27.
