A topological approach for human movement classification and anticipation
Communication avec acte
Auteur
BENSEKKA, Chakib Arslane 
CHRISTOPHE, GUILLETPOZZO, Thierry MERIENNE, FrédéricRésumé
The motion capture systems are increasingly used for biomedical purposes. In order to recognize and classify the movements, however whole-body movements using passive markers, generate a huge amount of data. Can topological data analysis methods improve the recognition of movements? Can we use the results of this analysis combined with particular types of neural networks to anticipate the continuation of a movement?
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Article dans une revue avec comité de lecture; ;CHRISTOPHE, GUILLET; ; MERIENNE, Frédéric (2015)
The objective of this paper is to present the advantages of Model reference adaptive control (MRAC) motion cueing algorithm against the classical motion cueing algorithm in terms of biome- chanical reactions of the ...
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Article dans une revue avec comité de lecture;CHRISTOPHE, GUILLET; ; ; MERIENNE, Frédéric (SAGE Publications, 2014)
This paper deals with driving simulation and in particular with the important issue of motion sickness. The paper proposes a methodology to evaluate the objective illness rating metrics deduced from the motion sickness ...
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Communication avec acte;CHRISTOPHE, GUILLET; ; MERIENNE, Frédéric (IEEE, 2012)
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