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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.
Cited literature [6 references]
https://hal.inria.fr/hal-01566578
Contributor : Hal Ifip <>
Submitted on : Friday, July 21, 2017 - 11:25:36 AM
Last modification on : Friday, July 21, 2017 - 11:30:45 AM
Contributor : Hal Ifip <>
Submitted on : Friday, July 21, 2017 - 11:25:36 AM
Last modification on : Friday, July 21, 2017 - 11:30:45 AM
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Distributed under a Creative Commons Attribution 4.0 International License