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Manipulative Tasks Identification by Learning and Generalizing Hand Motions

Abstract : In this work is proposed an approach to learn patterns and recognize a manipulative task by the extracted features among multiples observations. The diversity of information such as hand motion, fingers flexure and object trajectory are important to represent a manipulative task. By using the relevant features is possible to generate a general form of the signals that represents a specific dataset of trials. The hand motion generalization process is achieved by polynomial regression. Later, given a new observation, it is performed a classification and identification of a task by using the learned features.
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Diego R. Faria, Ricardo Martins, Jorge Lobo, Jorge Dias. Manipulative Tasks Identification by Learning and Generalizing Hand Motions. 2nd Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Feb 2011, Costa de Caparica, Portugal. pp.173-180, ⟨10.1007/978-3-642-19170-1_19⟩. ⟨hal-01566578⟩



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