Tactile data processing and analysis is still essentially an open challenge. In this framework, we demonstrate a method to achieve touch modality classification using pre-trained convolutional neural networks (CNNs). The 3D tensorial tactile data generated by real human interactions on an electronic skin (E-Skin) are transformed into 2D images. Using a transfer learning approach formalized through a CNN, we address the challenging task of the recognition of the object that was touched by the E-Skin. The feasibility and efficiency of the proposed method are proven using a real tactile dataset outperforming classification results obtained with the same dataset in the literature. © 2019 IEEE.

DCNN for Tactile Sensory Data Classification based on Transfer Learning

Alameh M.;Ibrahim A.;Valle M.;Moser G.
2019-01-01

Abstract

Tactile data processing and analysis is still essentially an open challenge. In this framework, we demonstrate a method to achieve touch modality classification using pre-trained convolutional neural networks (CNNs). The 3D tensorial tactile data generated by real human interactions on an electronic skin (E-Skin) are transformed into 2D images. Using a transfer learning approach formalized through a CNN, we address the challenging task of the recognition of the object that was touched by the E-Skin. The feasibility and efficiency of the proposed method are proven using a real tactile dataset outperforming classification results obtained with the same dataset in the literature. © 2019 IEEE.
2019
9781728135496
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/983359
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