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3D gesture classification with convolutional neural networks
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.
Cited literature [15 references]
https://hal.archives-ouvertes.fr/hal-01180542
Contributor : Stefan Duffner Connect in order to contact the contributor
Submitted on : Monday, July 27, 2015 - 1:50:08 PM
Last modification on : Monday, January 3, 2022 - 2:56:02 PM
Long-term archiving on: : Wednesday, October 28, 2015 - 10:51:34 AM
Contributor : Stefan Duffner Connect in order to contact the contributor
Submitted on : Monday, July 27, 2015 - 1:50:08 PM
Last modification on : Monday, January 3, 2022 - 2:56:02 PM
Long-term archiving on: : Wednesday, October 28, 2015 - 10:51:34 AM
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icassp2014.pdf
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