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Intrinsic Mode Entropy for postural steadiness analysisPostural balance during quiet standing is maintained by complex interactions of many sensory systems, including visual, vestibular, and proprioceptive systems. It has been demonstrated that applying vibration to the tibialis anterior tendon when subjects are in a static upright position creates an illusion of body inclination, thus decreasing postural stability. Postural balance was evaluated using centre of pressure (COP) displacements measured using a force plate. Recently, Intrinsic Mode Entropy (IMEn) has been proposed to quantify the degree of regularity and complexity in nonlinear signals. IMEn can be considered as an extension of Sample Entropy (SampEn) to deal with different oscillation levels. The first step of IMEn consists of extracting the Intrinsic Mode Functions (IMFs) of a time-series using Empirical Mode Decomposition (EMD). The IMEn is then obtained by computing the SampEN of the cumulative sums of the IMFs.
One-class support vector machine for joint variable selection and detection of postural balance degradationThe study of the static posture is of great interest for the analysis of the deficit of the control of balance. A method of balance analysis is to use a platform of forces which makes it possible to extract displacement of the centre of pressure (COP). The parameters extracted from COP time series prove like variables keys to supervise the degradation of balance. However, the irrelevance and\or the redundancy of some of them make difficult an effective detection of degradation. The objective of this paper is the implementation of a method of detection (SVDD) and of a procedure of selection of the relevant parameters able to detect a degradation of balance. The selected criterion of selection is the maximization of the area AUC under the curve ROC.
Validation of a smartphone gait analysis systemThis paper presents a validation study of a smartphone for detection of heel strike and foot flat during gait, in comparison with a validated in-shoe plantar pressure system. The aim of the study is to produce a smartphone gait analysis system that is able to estimate gait parameters in a non-controlled environment such as the home. The smartphone system using the built-in tri-axial accelerometer of the phone, and provides a reliable estimation of the number of steps and the stride-to-stride interval (ISI). Comparison with the results produced by an F-Scan mobile system showed an excellent relationship (R2=0.97). When Detrended Fluctuation Analysis was applied to the ISI calculated for each system, no significant differences were observed for a paired t-test. These findings open the way for other gait features such as gait velocity, walking distance and step length to be calculated using smartphones. Such a technique could be used to detect the loss of complexity in signals due to advanced age or disease in order to assess frailty and risk of falls in the elderly in ecological conditions.