BLSTM-RNN based 3D Gesture Classification

Grégoire Lefebvre (1) , Samuel Berlemont (2, 1) , Franck Mamalet (3) , Christophe Garcia (2)

1 Orange Labs [Meylan]
2 imagine - Extraction de Caractéristiques et Identification
3 Orange Labs [Cesson-Sévigné]

This paper presents a new robust method for inertial MEM (MicroElectroMechanical systems) 3D gesture recognition. The linear ac- celeration and the angular velocity, respectively provided by the accele- rometer and the gyrometer, are sampled in time resulting in 6D values at each time step which are used as inputs for the gesture recognition system. We propose to build a system based on Bidirectional Long Short- Term Memory Recurrent Neural Networks (BLSTM-RNN) for gesture classification from raw MEM data. We also compare this system to a ge- ometric approach using DTW (Dynamic Time Warping) and a statistical method based on HMM (Hidden Markov Model) from filtered and de- noised MEM data. Experimental results on 22 individuals producing 14 gestures in the air show that the proposed approach outperforms classi- cal classification methods with a classification mean rate of 95.57% and a standard deviation of 0.50 for 616 test gestures. Furthermore, these experiments underline that combining accelerometer and gyrometer in- formation gives better results that using a single inertial description.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Réseau de neurones [cs.NE]

Grégoire Lefebvre, Samuel Berlemont, Franck Mamalet, Christophe Garcia. BLSTM-RNN based 3D Gesture Classification. Artificial Neural Networks and Machine Learning, ICANN 2013, 23rd International Conference on Artificial Neural Networks, Sep 2013, Sofia, Bulgaria. ⟨hal-01224806⟩