Abstract
This paper presents a pattern discrimination method for electromyogram (EMG) signals for application in the field of prosthetic control. The method uses a novel recurrent neural network based on the hidden Markov model. This network includes recurrent connections, which enable modeling time series, such as EMG signals. Weight coefficients in the network can be learned using a well-known back-propagation through time algorithm. Pattern discrimination experiments were conducted to demonstrate the feasibility and performance of the proposed method. We were able to successfully discriminate forearm motions using the EMG signals, and achieved considerably high discrimination performance compared with other discrimination methods.
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Fukuda, O., Tsuji, T., and Kaneko, M. (1995). Pattern Classification of EEG Signals Using a Log-Linearized Gaussian Mixture Neural Networks. In Proc. IEEE International Conf. Neural Networks 1995, Vol. V (pp. 1113–1115). Perth, Australia.
Fukuda, O., Tsuji, T., and Kaneko, M. (2000). A Human Supporting Manipulator Based on Manual Control Using EMG Signals. Journal of the Robotics Society of Japan, 18(3), 387–394 (in Japanese).
Graupe, D., Magnussen, J., and Beex, A.A.M. (1978). A Microprocessor System for Multifunctional Control of Upper Limb Prostheses via Myo Electric Signal Identification. IEEE Trans. on Automatic Control, 23(4), 538–544.
Hiraiwa, A., Shimohara, K., and Tokunaga,Y. (1989). EMGPattern Analysis and Classification by Neural Network. In Proc. of IEEE International Conf. on Syst., Man and Cybern. (pp. 1113–1115).
Hiraiwa, A., Uchida, U., and Shimohara, K. (1992). EMG/EEG Pattern Recognition by Neural Networks. Proc. of the Eleventh European Meeting on Cybernetics and Systems Research (pp. 1383–1390).
Huang, H.P. and Chen, C.Y. (1999). Development of a Myoelectric Discrimination System for a Multi-Degree Prosthetic Hand. In Proc. of the 1999 IEEE International Conf. on Robotics and Automation (pp. 2392–2397). Detroit, USA.
Kelly, M.F., Parker, P.A., and Scott, R.N. (1990). The Application of Neural Networks to Myoelectric Signal Analysis: A Preliminary Study. IEEE Trans. on Biomedical Engineering, 37(3), 221–230.
Koike, Y. and Kawato, M. (1994). Estimation of Arm Posture in 3D-Space from Surface EMG Signals Using a Neural Network Model. Transactions Institute of Electronics, Information and Communication Engineers, J77-D(4), 368–375 (in Japanese).
Rabiner, L.R. (1989). A Tutorial on Hidden Markov Model and Selected Applications in Speech Recognition. Proc. of the IEEE, 77(2), 257–286.
Tsuji, T., Bu, N., Fukuda, O., and Kaneko, M. (2003). A Recurrent Log-Linearized Gaussian Mixture Network, IEEE Trans. Neural Networks, 14(2), 304–316.
Tsuji, T., Bu, N., Murakami, M., and Kaneko, M. (2001). Pattern Discrimination of Raw EMG Signals Using a New Recurrent Neural Network. In Proc. of IMEKO/SICE/IEEE the Int. Symp. on Measurement, Analysis and Modeling of Human Functions (pp.96–101). Sapporo, Japan.
Tsuji, T., Fukuda, O., Ichinobe, H., and Kaneko, M. (1999). A Log-Linearized Gaussian Mixture Network and its Application to EEG Pattern Classification. IEEE Trans. Systems, Man, and Cybernetics-Part C: Applications and Reviews, 29(1), 60–72.
Tsuji, T., Ichinobe, H., Ito, K., and Nagamachi, M. (1993). Discrimination of Forearm Motions from EMG Signals by Error Back Propagation Typed Neural Network Using Entropy. Transactions of the Society of Instrument and Control Engineers, 29(10), 1213–1220 (in Japanese).
Tsuji, T., Ito, K., and Nagamachi, M. (1987). A Limb-Function Discrimination Method Using EMG Signals for the Control of Multifunctional Powered Prostheses. Transactions Institute of Electronics, Information and Communication Engineers, J70-D(1), 207–215 (in Japanese).
Tsuji, T., Mori, D., and Ito, K. (1992). Motion Discrimination Method from EMG Signals Using Statistically Structured Neural Networks. The Transactions of The Institute of Electrical Engineers of Japan, 112-C(8), 465–473 (in Japanese).
Werbos, P.J. (1990). Backpropagation Through Time: What it Does and How to do it. Proc. of the IEEE, 78(10), 1550–1560.
Zak, M. (1988). Terminal Attractors for Addressable Memory in Neural Networks. Physics Letters A, 133(1/2), 18–22.
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Bu, N., Fukuda, O. & Tsuji, T. EMG-Based Motion Discrimination Using a Novel Recurrent Neural Network. Journal of Intelligent Information Systems 21, 113–126 (2003). https://doi.org/10.1023/A:1024706431807
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DOI: https://doi.org/10.1023/A:1024706431807