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.
|Title of host publication||DoCEIS'11: Technological Innovation for Sustainability, IFIP Advances in Information and Communication Technology|
|Number of pages||8|
|Publication status||Published - 2011|
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