On the detection of elderly equilibrium degradation using multivariate-EMD

International audienceThe aim of this paper is to provide a new methodology for the detection of an increased risk of falling in community-dwelling elderly. A new extended method of the empirical mode decomposition (EMD) called multivariate-EMD is employed in the proposed solution. This method will be mainly used to analyze the stabilogram center of pressure (COP) time series. In this paper, we describe also the remote non-invasive assessment method, which is suitable for static and dynamic balance. Balance was assessed using a miniature force plate, while gait was assessed using wireless sensors placed in a corridor of the home. The experimental results show the effectiveness of this indicator to identify the differences in standing posture between different groups of population

Traitement non linéaire des données pour l'analyse de l'équilibre chez les personnes âgées

Dans les pays développés, les dernières statistiques indiquent clairement l augmentation de la proportion des personnes âgées. Donc, le maintien de l'autonomie à domicile constitue aujourd'hui un enjeu primordial pour cette catégorie. En particulier, la majorité des personnes âgées, vivant seul à domicile, fait face à des situations à risques telles que des chutes. Dans cette thèse, nous proposons des méthodes non linéaires dans le but d'extraire des paramètres pertinents pour la prévention et la détection précoce du risque de chute. L originalité de ces méthodes réside dans l analyse non linéaire des composantes du signal obtenues à partir d une décomposition modale empirique. La décomposition modale empirique a permis de mettre en évidence l existence d une composante chaotique dans le signal à partir de laquelle on peut extraire un indicateur performant de la qualité de la posture humaine. Nous nous sommes particulièrement intéressés à la reformulation mathématique de l EMD dans le but est de la généraliser au cas multidimensionnel. Cette nouvelle extension a été appliquée par la suite sur les signaux stabilométriques dans le but d'extraire des paramètres pertinents de prévention et de détection de chute. Les perspectives de ce travail sont nombreuses, parmi lesquelles la fusion des données de la posture humaine avec d autres données issues des capteurs intégrés dans un habitat intelligentIn developed countries, the latest statistics clearly indicate that elderly population is growing, and people are living longer. Thus, maintaining a functional independence of the elderly becomes a priority for our society. The majority of elderly people living alone are more and more exposed to risky situations; one of them is the risk of falling. Falls are the leading cause of serious and health threats in the elder population. In this thesis, we started by reviewing the problem of fall detection in the elderly. Next, we proposed non-linear methods in order to extract relevant parameters for the prevention and early detection of the risk of falling. The originality of these methods is the nonlinear analysis of the components obtained from an empirical modal decomposition. The EMD method has highlighted the existence of a chaotic component in the signal from which we can extract an effective indicator of the quality of human posture. We are particularly interested in the mathematical reformulation of the EMD method in order to generalize it to the multidimensional case. This new extension was applied later on stabilogram signals in the aim of extracting relevant parameters for the prevention and detection of falling. One of the perspectives of this work is to consider data fusion of human posture with other data from sensors embedded in a smart homeTROYES-SCD-UTT (103872102) / SudocSudocFranceF

On the surface EMG signal reconstruction using blind source separation

International audienceIn biomedical signal processing, many sources are often mixed as a form of measured signal. The goal is usually to extract and analyze one or several of them separately. In the multichannel measurements, several Blind Source Separation (BSS) techniques are available for decomposing the signal into its components. In this paper, a novel method is presented for the reconstruction of individual muscle source signals from simulated surface Elec-tromyography (s-EMG) array recordings. This method is based on BSS in a Bayesian model selection framework. Specifically, it is relies on an efficient wavelet spectral matching separating algorithm. Our concept is evaluated on theoretical decomposition and is confirmed by simulated signals

Neodymium(III) removal by functionalized magnetic nanoparticles

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Watermarking Scar as an Ultimate copy protection

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Empirical Mode Decomposition for vectorial bi-dimensional signals

International audienceIn this work, an Empirical Mode Decomposition (EMD) of bivariate signals is proposed. It permits to decompose signals into elementary rotating functions, oscillating functions and tendencies. The technique is based on recursive extraction of non planar and rapidly rotating functions, and then a recursive extraction of rapidly oscillating functions as in case of univariate EMD. Our concept is based on a theoretical decomposition and is confirmed on simulated signals

Polymer Electrolyte Fuel Cell Stack Modeling and Temporal-Spatial Experimental Validations

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Blind separation of noisy convolutive sources

International audienceIn this paper, we present a new blind source separation method for noisy linear convolutive signal mixtures of independent components. The effectiveness of the proposed method is tested on synthetic data. Our technique has various advantages. First, it is based on the expectation-maximization (EM) algorithm for the separation of the components. Second, the proposed technique works in the spectral domain where, thanks to two simple approximations, the likelihood assumes a simple form which is easy to handle (low dimensional sufficient statistics) and to maximize (via the EM algorithm). Using this technique, we have obtained a good preliminary results being able to blindly separate noisy mixtures with two components and four different versions of mixing matrix