3D GESTURE CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS

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 convolutional 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.