Discrete wavelet and modified PCA decompositions for postural stability analysis in biometric applications

Abstract

The aim of this study is to compare the Discrete wavelet decomposition and the modified Principal Analysis Component (PCA) decomposition to analyze the stabilogram for the purpose to provide a new insight about human postural stability. Discrete wavelet analysis is used to decompose the stabilogram into several timescale components (i.e. detail wavelet coefficients and approximation wavelet coefficients). Whereas, the modified PCA decomposition is applied to decompose the stabilogram into three components, namely: trend, rambling and trembling. Based on the modified PCA analysis, the trace of analytic trembling and rambling in the complex plan highlights a unique rotation center. The same property is found when considering the detail wavelet coefficients. Based on this property, the area of the circle in which 95% of the trace's data points are located, is extracted to provide important information about the postural equilibrium status of healthy subjects (average age 31 ± 11 years). Based on experimental results, this parameter seems to be a valuable parameter in order to highlight the effect of visual entries, stabilogram direction, gender and age on the postural stability. Obtained results show also that wavelets and the modified PCA decomposition can discriminate the subjects by gender which is particularly interesting in biometric applications and human stability simulation. Moreover, both techniques highlight the fact that male are less stable than female and the fact that there is no correlation between human stability and his age (under 60).

Analysis of sensory system aspects of postural stability during quiet standing in adolescent idiopathic scoliosis patients

“…The DWT method for CoP signal decomposition has been applied in various fields. It has been used to analyze difference of postural stability based upon gender [ 45 ], determine effect of vestibular dysfunction on postural control [ 46 ], and evaluate difference of postural control ability after cochlear implant surgery [ 47 ]. In this study, we analyzed the difference of postural instability between AIS group ( n = 32) and control group ( n = 25) of the similar age, and interpreted differences in sensory information in terms of visual, vestibular, and somatosensory systems.…”

Effects of kinesthetic haptic feedback on standing stability of young healthy subjects and stroke patients

“…( 3 ) and ( 4 ). Similar to our approach, other researchers have used trunk tilt projection derived from an electromagnetic sensor, identified balance and stability behavior, and classified individuals on the basis of age, gender, height and weight [ 32 - 35 ]. Mean Velocity Displacement, Planar Deviation, and the ML and AP Trajectories [Eqns.…”

The Filtering of the Posturographic Signals Shows the Age Related Features

“…Amoud et al [ 8 ] showed the differences between age groups both using basic-EMD with separate analysis for x and y components and using complex-EMD where the interdependence between x and y components is not removed. Discrete wavelet decomposition and modified PCA to decompose the stabilogram into three components were proposed by Maatar et al [ 10 ]. He called these components: trend, rambling, and trembling.…”