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    Human-Animal Affective Robot Touch Classification Using Deep Neural Network

    Mohammed Ibrahim Ahmed Al-mashhadani1, Theyazn H. H. Aldhyani2,*, Mosleh Hmoud Al-Adhaileh3, Alwi M. Bamhdi4, Mohammed Y. Alzahrani5, Fawaz Waselallah Alsaade6, Hasan Alkahtani1,6

    Computer Systems Science and Engineering, Vol.38, No.1, pp. 25-37, 2021, DOI:10.32604/csse.2021.014992

    Abstract Touch gesture recognition is an important aspect in human–robot interaction, as it makes such interaction effective and realistic. The novelty of this study is the development of a system that recognizes human–animal affective robot touch (HAART) using a deep learning algorithm. The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015. The dataset was tested with numerous subjects performing different HAART gestures; each touch was performed on a robotic animal covered by a pressure sensor skin. A convolutional neural network algorithm is proposed to implement the touch… More >

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