Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals
1721 - 1743
Item Usage Stats
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
Showing items related by title, author, creator and subject.
Directionally selective fractional wavelet transform using a 2-d non-separable unbalanced lifting structure Keskin, Furkan; Çetin, A. Enis (Springer, Berlin, Heidelberg, 2012)In this paper, we extend the recently introduced concept of fractional wavelet transform to obtain directional subbands of an image. Fractional wavelet decomposition is based on two-channel unbalanced lifting structures ...
Erden, F.; Alkar, A. Z.; Çetin, A. Enis (Elsevier BV, 2015)Respiratory rate is an essential parameter in many practical applications such as apnea detection, patient monitoring, and elderly people monitoring. In this paper, we describe a novel method and a contact-free multi-modal ...
Bala, E.; Çetin, A. Enis (IEEE, 2003)In this paper, an affine invariant function is presented for object recognition from wavelet coefficients of the object boundary. In previous works, undecimated wavelet transform was used for affine invariant functions. ...