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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Communication dans un congrès
Luis M. Camarinha-Matos. 2nd Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Feb 2011, Costa de Caparica, Portugal. Springer, IFIP Advances in Information and Communication Technology, AICT-349, pp.173-180, 2011, Technological Innovation for Sustainability. 〈10.1007/978-3-642-19170-1_19〉
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Diego Faria, Ricardo Martins, Jorge Lobo, Jorge Dias. Manipulative Tasks Identification by Learning and Generalizing Hand Motions. Luis M. Camarinha-Matos. 2nd Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Feb 2011, Costa de Caparica, Portugal. Springer, IFIP Advances in Information and Communication Technology, AICT-349, pp.173-180, 2011, Technological Innovation for Sustainability. 〈10.1007/978-3-642-19170-1_19〉. 〈hal-01566578〉

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