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

Sensors

Birsel Ayrulu-Erdem¹ 

[1] Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, 06800 Ankara, Turkey; E-Mail

关键词: leg motion classification;    inertial sensors;    gyroscopes;    accelerometers;    discrete wavelet transform;    wavelet decomposition;    feature extraction;    pattern recognition;    artificial neural networks;   

DOI  :  10.3390/s110201721

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.