Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals
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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 DWT decomposition and reconstruction. © 2011 by the authors; licensee MDPI, Basel, Switzerland.
Artificial neural networks
Discrete wavelet transform
Leg motion classification
artificial neural network
Neural Networks (Computer)
Signal Processing, Computer-Assisted
Published Version (Please cite this version)10.3390/s110201721
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