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3D gesture classification with convolutional neural networks

Stefan Duffner 1 Samuel Berlemont 2, 1 Grégoire Lefebvre 2 Christophe Garcia 1 
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this paper, we present an approach that classifies 3D gestures using jointly accelerometer and gyroscope signals from a mobile device. The proposed method is based on a convo-lutional neural network with a specific structure involving a combination of 1D convolution, averaging, and max-pooling operations. It directly classifies the fixed-length input matrix , composed of the normalised sensor data, as one of the gestures to be recognises. Experimental results on different datasets with varying training/testing configurations show that our method outperforms or is on par with current state-of-the-art methods for almost all data configurations.
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Stefan Duffner, Samuel Berlemont, Grégoire Lefebvre, Christophe Garcia. 3D gesture classification with convolutional neural networks. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014, Florence, Italy. pp.5432 - 5436, ⟨10.1109/ICASSP.2014.6854641⟩. ⟨hal-01180542⟩



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