Abstract
sEMG is biological signal produced by muscular. According to the characteristics of myoelectric signal, FIR model of the single input and multiple outputs was proposed in this paper. Due to the unknown input of the model, instrumental variable with blind identification was used to identify the model’s transfer function. The parameters of model were used as input of neural network to classify six types of forearm motions: extension of thumb, extension of wrist, flexion of wrist, fist grasp, side flexion of wrist, extension of palm. The experimental results demonstrate that this method has better classification accuracy than the classical AR method.
This paper is supported by the Key Project of Science and Technology Development Plan of Jilin Province (Grant No.20090350), Chinese College Doctor Special Scientific Research Fund (Grant No.20100061110029) and the Jilin University "985 project" Engineering Bionic Sci. & Tech. Innovation Platform.
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References
Jin, D.W., Wang, R.C.: Artificial lntelligent prosthesis. Chinese J. Clinical Rehabilitation 5(20), 2994–2995 (2002)
Doerschuk, P.C., Gustafson, D.E., Willsky, A.S.: Upper extremity limb function discrimination using EMG signal analysis. IEEE Transactions on Biomedical Engineering 30(1), 18–29 (1983)
Cai, L., Wang, Z., Liu, Y.: Modelling and Classification of Two-Channel elect romyography Signals Based on Blind Channel Identification Theory. Journal of Shang Hai Jiao Tong University 34(11), 1468–1471 (2000)
Deluca, C.: Physiology and mathematics of myoelectric signals. IEEE Transactions on Biomedical Engineering 26(6), 313–325 (1979)
Luo, H., Li, Y.D.: Application of blind channel identification techniques to restack seismic deconvolution. Proceedings of IEEE 86(10), 2082–2089 (1998)
Ding, F., Chen, T.: Identification of Hammerstein nonlinear AR-MAX systems. Automatica 41(9), 1479–1489 (2005)
Shang, X., Tian, Y., Li, Y., Wang, L.: The Recognition of Gestures and Movements Based On MPNN. Journal of Jilin University (Information Science Edition) 28(5), 459–466 (2010)
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Shang, X., Tian, Y., Li, Y. (2011). Modeling and Classification of sEMG Based on Instrumental Variable Identification. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21111-9_37
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DOI: https://doi.org/10.1007/978-3-642-21111-9_37
Publisher Name: Springer, Berlin, Heidelberg
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