Manipulative Tasks Identification by Learning and Generalizing Hand Motions

Diego R. Faria, Ricardo Martins, Jorge Lobo, Jorge Dias

Research output: Chapter in Book/Report/Conference proceedingConference publication

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.
Original languageEnglish
Title of host publicationDoCEIS'11: Technological Innovation for Sustainability, IFIP Advances in Information and Communication Technology
EditorsL.M. Camarinha-Matos
PublisherSpringer
Pages173-180
Number of pages8
Volume349
ISBN (Electronic)978-3-642-19170-1
ISBN (Print)978-3-642-19169-5
DOIs
Publication statusPublished - 2011

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  • Cite this

    Faria, D. R., Martins, R., Lobo, J., & Dias, J. (2011). Manipulative Tasks Identification by Learning and Generalizing Hand Motions. In L. M. Camarinha-Matos (Ed.), DoCEIS'11: Technological Innovation for Sustainability, IFIP Advances in Information and Communication Technology (Vol. 349, pp. 173-180). Springer. https://doi.org/10.1007/978-3-642-19170-1_19