Abstract
Surface electromyographic (sEMG) signals from the residual limb muscles after amputation have been widely used for prosthesis control. However, for the amputees with high-level amputations, there usually exists a dilemma that the sEMG signal sources for prosthesis control are limited but more limb motions need to be recovered, which strongly limits the practicality of the current myoelectric prostheses. In order to operate prostheses with multiple degrees of freedom (DOF) of movements, several control protocols have been suggested in some previous studies to deal with this dilemma. In this paper, a prosthesis control system based on the combination of speech and sEMG signals (Strategy 1) was built up in laboratory conditions, where speech commands were applied for the prosthesis joint-mode switching and sEMG signals were applied to determine the motion-class and execute the target movement. The control performance of the developed system was evaluated and compared with that of the traditional control strategy based on the pattern recognition of sEMG signals (Strategy 2). The experimental results showed that the difference between Strategy 1 and Strategy 2 was insignificant for the control of a 2-DOF prosthesis, but Strategy 1 was much better in the control of a prosthesis with more DOFs in comparison to Strategy 2. In addition, the positive user experience also demonstrated the reliability and practicality of Strategy 1.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Graupe, D., Cline, W.K.: Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes. IEEE Transactions on Systems, Man, and Cybernetics 5(2), 252–259 (1975)
Zardoshi-Kermani, M., Wheeler, B.C., Badie, K., Hashemi, R.M.: EMG feature evaluation for movement control of upper extremity prostheses. IEEE Transactions on Rehabilitation Engineering 3(4), 324–333 (1995)
Tenore, F., Ramos, A., Fahmy, A., Acharya, S., Etienne-Cummings, R., Thakor, N.V.: Towards the control of individual fingers of a prosthetic hand using surface EMG signals. In: 29th Annual International Conference of the IEEE EMBS, Lyon, pp. 6145–6148 (2007)
Castellini, C., van der Smagt, P.: Surface EMG in advanced hand prosthetics. Biological Cybernetics 10, 35–47 (2009)
Parker, P.A., Scott, R.N.: Myoelectric control of prostheses. Critical Reviews in Biomedical Engineering 13(4), 283 (1986)
Williams III, T.W.: Practical methods for controlling powered upper-extremity prostheses. Assistive Technology 2(1), 3–18 (1990)
Young, A.J., Smith, L.H., Rouse, E.J., Hargrove, L.J.: Classification of simultaneous movements using surface EMG pattern recognition. IEEE Transactions on Biomedical Engineering 60(5), 1250–1258 (2013)
Li, G., Li, Y., Yu, L., Geng, Y.: Conditioning and sampling issues of EMG signals in motion recognition of multifunctional myoelectric prostheses. Annals of Biomedical Engineering 39(6), 1779–1787 (2011)
Fang, P., Wei, Z., Geng, Y., Yao, F., Li, G.: Using speech for mode Selection in control of multifunctional myoelectric prostheses. In: 35th Annual International Conference of the IEEE EMBS, Osaka, pp. 3602–3605 (2013)
Mubeen, N., Shahina, A., Khan, A.N., Vinoth, G.: Combining spectral features of standard and throat microphone signal for speaker recognition. In: 2012 International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, pp. 119–122 (2012)
Muda, L., Begam, M., Elamvazuthi, I.: Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. Journal of Computing 2(3) (2010)
Paul, A.K., Das, D., Kamal, M.M.: Bangla speech recognition system using LPC and ANN. In: 7th International Conference on Advances in Pattern Recognition, Kolkata, pp. 171–174 (2009)
Al-Faiz, M.Z., Ali, A.A., Miry, A.H.: A k-nearest neighbor based algorithm for human arm movements recognition using EMG signals. In: 1st International Conference on Energy, Power and Control (EPC-IQ), Basrah, pp. 159–167 (2010)
Bay, S.D.: Combining nearest neighbor classifiers through multiple feature subsets. In: ICML, vol. 98, pp. 37–45 (1998)
Chen, L., Geng, Y., Li, G.: Effect of upper-limb positions on motion pattern recognition using electromyography. In: 4th International Congress on Image and Signal Processing (CISP), Shanghai, vol. 1, pp. 139–142 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wei, Z., Fang, P., Tian, L., Zhuo, Q., Li, G. (2014). A Prosthesis Control System Based on the Combination of Speech and sEMG Signals and Its Performance Assessment. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-06269-3_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06268-6
Online ISBN: 978-3-319-06269-3
eBook Packages: Computer ScienceComputer Science (R0)