Abstract
In this paper, we aim for construction of high-speed and high-accurate system using Fast Fourier Transform (FFT) for feature extraction, Simple-PCA (SPCA) for feature compression, and a neural network (NN) for recognition. In particular, we present a novel method based on Multiple PCA to improve recognition accuracy for EMG. From results of computer simulation, it is shown that our approach is effective for improvement in recognition accuracy and speed.
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© 2004 Springer-Verlag Berlin Heidelberg
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Matsumura, Y., Fukumi, M., Akamatsu, N., Takeda, F. (2004). Wrist EMG Pattern Recognition System by Neural Networks and Multiple Principal Component Analysis. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_120
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DOI: https://doi.org/10.1007/978-3-540-30132-5_120
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
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