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 contributionpeer-review

1 Scopus citations


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 languageBritish English
Title of host publicationTechnological Innovation for Sustainability - Second IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2011, Proceedings
Number of pages8
StatePublished - 2011

Publication series

NameIFIP Advances in Information and Communication Technology
Volume349 AICT
ISSN (Print)1868-4238


  • Motion Patterns
  • Task Generalization
  • Task Recognition


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