Manipulative Tasks Identification by Learning and Generalizing Hand Motions

  • Diego R. Faria
  • Ricardo Martins
  • Jorge Lobo
  • Jorge Dias
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 349)

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.

Keywords

Motion Patterns Task Recognition Task Generalization 

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Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Diego R. Faria
    • 1
  • Ricardo Martins
    • 1
  • Jorge Lobo
    • 1
  • Jorge Dias
    • 1
  1. 1.Institute of Systems and Robotics, DEECUniversity of CoimbraPortugal

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