In this paper, different techniques of analysis have been used to study the effects of perturbations generated from a robotic mobile platform called Isiskate. These disturbances were applied on two categories of people: post-CVA subjects suffering from cerebrovascular accident and healthy individuals. Our aim is to analyze some assessment tools to distinguish between different postural behaviors. In relevant works, very few studies have addressed the use of nonlinear time-series methods in diagnosis of post-CVA pathological postural behavior. Furthermore, our tools are based on parametric and non-parametric identification procedures, that can yield to an insight on how to improve the examination time. As part of our analysis, the tests were established with several levels of sinusoidal vibrations, along the anterior–posterior (A/P) and medial–lateral (M/L) planes. The mobile platform allowed us to record a set of coordinates that includes center of pressure (COP) as a function of time. First, we have quantified some linear parameters and spectral characteristics using power spectral density (PSD). Thereafter, we have deduced stochastic parameters using stabilogram diffusion analysis (SDA), which revealed some interesting invariants. Then transfer functions between the platform velocity and COP trajectory were evaluated. They were carried out at frequencies from 0.1 Hz to 3.3 Hz. Furthermore, we accomplished a comparison of models based on both parametric and nonparametric identification methods. The combination of the proposed techniques has provided us an understanding of human control process by establishing a behavior model and helped us to distinguish patients with postural disorders. This improves postural analysis and facilitates the diagnosis of pathologies related to equilibrium which serves in rehabilitation.

In this paper, different techniques of analysis have been used to study the effects of perturbations generated from a robotic mobile platform called Isiskate. These disturbances were applied on two categories of people: post-CVA subjects suffering from cerebrovascular accident and healthy individuals. Our aim is to analyze some assessment tools to distinguish between different postural behaviors. In relevant works, very few studies have addressed the use of nonlinear time-series methods in diagnosis of post-CVA pathological postural behavior. Furthermore, our tools are based on parametric and non-parametric identification procedures, that can yield to an insight on how to improve the examination time. As part of our analysis, the tests were established with several levels of sinusoidal vibrations, along the anterior–posterior (A/P) and medial–lateral (M/L) planes. The mobile platform allowed us to record a set of coordinates that includes center of pressure (COP) as a function of time. First, we have quantified some linear parameters and spectral characteristics using power spectral density (PSD). Thereafter, we have deduced stochastic parameters using stabilogram diffusion analysis (SDA), which revealed some interesting invariants. Then transfer functions between the platform velocity and COP trajectory were evaluated. They were carried out at frequencies from 0.1 Hz to 3.3 Hz. Furthermore, we accomplished a comparison of models based on both parametric and nonparametric identification methods. The combination of the proposed techniques has provided us an understanding of human control process by establishing a behavior model and helped us to distinguish patients with postural disorders. This improves postural analysis and facilitates the diagnosis of pathologies related to equilibrium which serves in rehabilitation.

In this paper, different techniques of analysis have been used to study the effects of perturbations generated from a robotic mobile platform called Isiskate. These disturbances were applied on two categories of people: post-CVA subjects suffering from cerebrovascular accident and healthy individuals. Our aim is to analyze some assessment tools to distinguish between different postural behaviors. In relevant works, very few studies have addressed the use of nonlinear time-series methods in diagnosis of post-CVA pathological postural behavior. Furthermore, our tools are based on parametric and non-parametric identification procedures, that can yield to an insight on how to improve the examination time. As part of our analysis, the tests were established with several levels of sinusoidal vibrations, along the anterior–posterior (A/P) and medial–lateral (M/L) planes. The mobile platform allowed us to record a set of coordinates that includes center of pressure (COP) as a function of time. First, we have quantified some linear parameters and spectral characteristics using power spectral density (PSD). Thereafter, we have deduced stochastic parameters using stabilogram diffusion analysis (SDA), which revealed some interesting invariants. Then transfer functions between the platform velocity and COP trajectory were evaluated. They were carried out at frequencies from 0.1 Hz to 3.3 Hz. Furthermore, we accomplished a comparison of models based on both parametric and nonparametric identification methods. The combination of the proposed techniques has provided us an understanding of human control process by establishing a behavior model and helped us to distinguish patients with postural disorders. This improves postural analysis and facilitates the diagnosis of pathologies related to equilibrium which serves in rehabilitation.

In this paper, different techniques of analysis have been used to study the effects of perturbations generated from a robotic mobile platform called Isiskate. These disturbances were applied on two categories of people: post-CVA subjects suffering from cerebrovascular accident and healthy individuals. Our aim is to analyze some assessment tools to distinguish between different postural behaviors. In relevant works, very few studies have addressed the use of nonlinear time-series methods in diagnosis of post-CVA pathological postural behavior. Furthermore, our tools are based on parametric and non-parametric identification procedures, that can yield to an insight on how to improve the examination time. As part of our analysis, the tests were established with several levels of sinusoidal vibrations, along the anterior–posterior (A/P) and medial–lateral (M/L) planes. The mobile platform allowed us to record a set of coordinates that includes center of pressure (COP) as a function of time. First, we have quantified some linear parameters and spectral characteristics using power spectral density (PSD). Thereafter, we have deduced stochastic parameters using stabilogram diffusion analysis (SDA), which revealed some interesting invariants. Then transfer functions between the platform velocity and COP trajectory were evaluated. They were carried out at frequencies from 0.1 Hz to 3.3 Hz. Furthermore, we accomplished a comparison of models based on both parametric and nonparametric identification methods. The combination of the proposed techniques has provided us an understanding of human control process by establishing a behavior model and helped us to distinguish patients with postural disorders. This improves postural analysis and facilitates the diagnosis of pathologies related to equilibrium which serves in rehabilitation.

Ce projet de thèse s’intéresse à la problématique de la modélisation analytique de la planification de production et des capacités, des stratégies de sous-traitance, de la politique de maintenance et de contrôle de la qualité et à son optimisation grâce à des modèles plus aptes à prendre en compte le couplage entre ces fonctions. Quelques études existent déjà, mais elles n’intègrent cependant pas toujours tous les paramètres, ni la collaboration qui peut se créer au sein d’une chaîne logistique. L’idée est de proposer des modèles les plus généraux possible, et surtout facilement adaptable aux données réelles de l’entreprise, ces modèles seront ensuite utilisés pour évaluer les performances et optimiser ces stratégies. Le développement durable est un sujet en plein essor du fait des enjeux sociétaux qui y sont attachés. Nous nous proposons d’intégrer plusieurs questions environnementales qui peuvent être liées à la production, à la sous-traitance, à la maintenance et à la qualité. L’objectif est d’évaluer les implications environnementales de la chaîne dans un contexte de la logistique inverse. Ces contributions, ainsi que les coûts associés ne peuvent être ignorées dans nos modèles. Dans nos travaux, les méthodes exactes seront utilisées pour résoudre et valider des problèmes. Parfois, il est difficile de résoudre des problèmes complexes en utilisant les techniques d'optimisation classiques. La complexité augmente également avec l'augmentation du nombre de paramètres et/ou des variables de décision. Dans de tels cas, des algorithmes heuristiques ou métaheuristiques seront utilisés, ces algorithmes sont jugés plus efficaces pour générer une solution optimale/quasi-optimale pour des problèmes complexes avec des temps de calcul moins. Intégrer dans les modèles d’optimisation l’aspect incertain pourra apporter des réponses plus réalistes que celles disponibles à l’heure actuelle. La logique floue, la théorie des probabilités floues ou des possibilités peuvent être adaptés pour prendre en compte les contraintes spécifiquement industrielles. Une telle approche est originale et peu de publications traitent actuellement cette problématique.