Ayrulu-Erdem, B.Barshan, B.2016-02-082016-02-08201114248220http://hdl.handle.net/11693/22032We 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.EnglishAccelerometersArtificial neural networksDiscrete wavelet transformFeature extractionGyroscopesInertial sensorsLeg motion classificationPattern recognitionWavelet decompositionalgorithmarticleartificial neural networkhumaninstrumentationlegmotionphysiologysignal processingwavelet analysisAlgorithmsHumansLegMotionNeural Networks (Computer)Signal Processing, Computer-AssistedWavelet AnalysisArticle10.3390/s110201721