Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals.
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.
Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, 06800 Ankara, Turkey. ebirsel@gmail.com
Birsel
B
Ayrulu-Erdem
Billur
B
Barshan
D000465:Algorithms; D006801:Humans; D007866:Leg / Q000502:physiology*; D009038:Motion; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D058067:Wavelet Analysis