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Sensors 2011, 11(2), 1721-1743; doi:10.3390/s110201721

Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals

Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, 06800 Ankara, Turkey
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Received: 14 December 2010 / Revised: 10 January 2011 / Accepted: 13 January 2011 / Published: 28 January 2011
(This article belongs to the Section Physical Sensors)
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Abstract

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWTdecomposition and reconstruction. View Full-Text
Keywords: leg motion classification; inertial sensors; gyroscopes; accelerometers; discrete wavelet transform; wavelet decomposition; feature extraction; pattern recognition; artificial neural networks leg motion classification; inertial sensors; gyroscopes; accelerometers; discrete wavelet transform; wavelet decomposition; feature extraction; pattern recognition; artificial neural networks
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Ayrulu-Erdem, B.; Barshan, B. Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals. Sensors 2011, 11, 1721-1743.

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