Добірка наукової літератури з теми "Rattrapage de chutes"
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Статті в журналах з теми "Rattrapage de chutes":
Armand-Privat, Pillah Niali. "La Femme et l’Avenir de l’Afrique Subsaharienne (à Mes Soeurs et Frères)." European Scientific Journal ESJ 1 (January 30, 2023). http://dx.doi.org/10.19044/esipreprint.1.2023p394.
Armand-Privat, Pillah Niali. "La Femme et l’Avenir de l’Afrique Subsaharienne (à Mes Soeurs et Frères)." European Scientific Journal ESJ 1 (January 21, 2023). http://dx.doi.org/10.19044/esipreprint.1.2023.394.
Дисертації з теми "Rattrapage de chutes":
Tisserand, Romain. "Mécanismes du rattrapage de l’équilibre et évaluation du risque de chute chez une population âgée autonome." Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10240/document.
Falling is a common and concerning health problem for the elderly population. This research work focuses on the characterization of the biomechanical and cognitive strategies involved in the balance maintain and balance recovery that help elderly to avoid a fall. Particularly, we interested in a community-dwelling elderly population, in order to identify the persons who are at risk of fall and suggest a forward preventive intervention. We show, for this population, that usual clinical tests do not well discriminate between “fallers” and “non-fallers” and that the fall problem is more concerned by cognitive and/or sensorial troubles than by muscular troubles that affect biomechanical responses. The most discriminant tests are identified and a risk of fall assessment tool is suggested to give informations about the deficient mechanisms. Finally, we provide informations about the mechanisms involved in protective steps, a prevalent balance strategy which not used in balance clinical assessments
Vallée, Pascal. "Estimation du risque de chute suite à une perturbation d’équilibre." Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10261/document.
Falling is the second most important cause of accidental deaths in the world. These falls lead to physical injuries and psychological consequences which limit mobility especially for the elderly. Being able to prevent these events appears to be crucial and a first mandatory step is to identify the risk of fall depending on the situation (slipping, hazardous environment …). The objective of this thesis is to propose numerical tools in order to link the risk of fall to the balance perturbation. In a first part, a simple numerical model estimates if initial unbalanced states can be recovered using one recovery step. Then a focus was made on external perturbations with a specific temporal profile close to public transportation perturbations. An experimental protocol was set up due to the lack of knowledge about these perturbations. The experiment investigated the effect of the perturbation parameters on the Balance Recovery (BR) threshold. An adjustment of the previous model was made to evaluate continuous perturbations and to compare its results against our experimental findings. The final chapter developed a complementary approach based on previous work using a model predictive control scheme which aims to regulate BR strategies. This whole work contributes to highlight the BR parameters which are difficult to assess during experimental procedures. It also points out the perturbation definition which is frequently incomplete in the literature although it is the phenomenon responsible for the BR process. In order to represent more realistic human behaviors some improvements can be done such as adding noncoplanar contacts or as integrating more detailed sensorimotor aspects
Nowakowski, Katharine. "The prediction and management of muscle ageing : 3D musculoskeletal simulations and multi-scale biomechanical modeling for the analysis of human falls and fall prevention strategies through the application of artificial intelligence approaches." Electronic Thesis or Diss., Compiègne, 2023. http://www.theses.fr/2023COMP2763.
The age-related decline in muscle function is linked to both sarcopenia and an increased risk for falls. In this doctoral project, an analysis of the morphological, functional, mechanical and biophysical parameters known to be affected by ageing is presented. The data has been analysed with statistical and machine learning techniques. These results influenced the development of a deep reinforcement learning simulation for both young adult and elderly falls, based on the parameters sensitive to ageing such as maximum isometric force, contraction velocity, deactivation time, passive muscle strain, hip extension range and a mass shift from the legs to the trunk. Testing of the sensitivity of the results then led to the development of a coupled simulation to study falls recovery, where the effects of sensory nerves and proprioception was considered. The strategy for coupling allows for recovery for any fall position to be analysed to further test the limits of recovery produced by the given model. The results from each aspect of the project suggest that muscle ageing can be further elucidated through the development of a multi-scale model that could consider fatigue and the effect of biophysical changes on movement outcomes. A multi-scale model, where agent-based modelling is coupled to a reinforcement learning environment is proposed. The model accounts for the conversion of type II muscle fibres to type I fibres, as well as considers the dynamics of calcium, inorganic phosphate, and ATP, with prospective for further adaptations. This work demonstrates the interest in further exploration of complex human system modelling by leveraging artificial intelligence techniques