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• W3201799239 abstract "Action recognition has been gaining interest in research due to its great number of applications. So, the main contribution of this manuscript is a human–robot interaction framework, relying on dimension reduction of the system’s inputs in order to require a smaller dataset for training of an Artificial Neural Network. Our motivation is the development of a Social Assistive Robotics application. In summary, we choose nine standard actions to guide a robot, and two neutral ones to represent stand-by or resting cases. The dataset is created by people with different body shape, for robustness purposes, using only 5 to 10 samples of each class per person. Offline and online tests validate the method’s accuracy and confusion matrices clarify the results. A TicTacToe game using a ground robot exemplify a real world application, where each action represents a desired spot in the game. The results confirm a high accuracy, above 96.7%, in all the tests. Based on this, we can conclude our preprocessing strategy and classifier are capable of identifying the action patterns, even for a tiny dataset; thus, it is recommended for educational proposals due to its simplicity." @default.
• W3201799239 created "2021-10-11" @default.
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• W3201799239 date "2022-01-01" @default.
• W3201799239 modified "2023-10-17" @default.
• W3201799239 title "Action recognition for educational proposals applying concepts of Social Assistive Robotics" @default.
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• W3201799239 doi "https://doi.org/10.1016/j.cogsys.2021.09.002" @default.
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