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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; in revised form: 10 January 2011 / Accepted: 13 January 2011 / Published: 28 January 2011
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.
Keywords: leg motion classification; inertial sensors; gyroscopes; accelerometers; discrete wavelet transform; wavelet decomposition; feature extraction; pattern recognition; artificial neural networks
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Cite This Article
MDPI and ACS Style
Ayrulu-Erdem, B.; Barshan, B. Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals. Sensors 2011, 11, 1721-1743.
Ayrulu-Erdem B, Barshan B. Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals. Sensors. 2011; 11(2):1721-1743.
Ayrulu-Erdem, Birsel; Barshan, Billur. 2011. "Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals." Sensors 11, no. 2: 1721-1743.