F. G. Hofmann, P. Heyer, and G. Hommel, Velocity profile based recognition of dynamic gestures with discrete Hidden Markov Models, Gesture and Sign Language in Human-Computer Interaction, pp.81-95, 1998.
DOI : 10.1007/BFb0052991

S. Kallio, J. Kela, and J. Mantyjarvi, Online gesture recognition system for mobile interaction, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme, System Security and Assurance (Cat. No.03CH37483), pp.2070-2076, 2003.
DOI : 10.1109/ICSMC.2003.1244189

J. Kela, P. Korpipää, J. Mäntyjärvi, S. Kallio, G. Savino et al., Accelerometer-based gesture control for a design environment, Personal and Ubiquitous Computing, pp.285-299, 2006.
DOI : 10.1007/s00779-005-0033-8

T. Pylvänäinen, Accelerometer Based Gesture Recognition Using Continuous HMMs Pattern Recognition and Image Analysis, Lecture Notes in Computer Science, vol.3522, pp.413-430, 2005.

D. H. Wilson and A. Wilson, Gesture recognition using the xwand, 2004.

J. Liu, Z. Wang, L. Zhong, J. Wickramasuriya, and V. Vasudevan, uWave: Accelerometerbased personalized gesture recognition and its applications, International Conference on Pervasive Computing and Communications, pp.1-9, 2009.

A. Akl and S. Valaee, Accelerometer-based gesture recognition via dynamic-time warping, affinity propagation, & compressive sensing, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010.
DOI : 10.1109/ICASSP.2010.5495895

M. Hoffman, P. Varcholik, and J. J. Laviola, Breaking the status quo: Improving 3D gesture recognition with spatially convenient input devices, 2010 IEEE Virtual Reality Conference (VR), pp.59-66, 2010.
DOI : 10.1109/VR.2010.5444813

J. Wu, G. Pan, D. Zhang, G. Qi, and S. Li, Gesture Recognition with a 3-D Accelerometer, Ubiquitous Intelligence and Computing, pp.25-38, 2009.
DOI : 10.1007/s00779-005-0033-8
URL : https://hal.archives-ouvertes.fr/hal-00792994

G. Lefebvre, S. Berlemont, F. Mamalet, and C. Garcia, BLSTM-RNN Based 3D Gesture Classification, Proceedings of the International Conference on Artificial Neural Networks, pp.381-388, 2013.
DOI : 10.1007/978-3-642-40728-4_48
URL : https://hal.archives-ouvertes.fr/hal-01224806

A. Graves, J. Schmidhuber-lecun, L. Bottou, Y. Bengio, and P. Haffner, Framewise phoneme classification with bidirectional LSTM and other neural network architectures Gradient-based learning applied to document recognition, IEEE Transactions on Neural Networks Proceedings of the IEEE, vol.12, issue.86 11, pp.5-6, 1998.

Y. Lecun, K. Kavukvuoglu, and C. Farabet, Convolutional networks and applications in vision, Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010.
DOI : 10.1109/ISCAS.2010.5537907

S. Cho, E. Choi, W. Bang, J. Yang, J. Sohn et al., Two-stage recognition of raw acceleration signals for 3-D gesture-understanding cell phones, 10th International Workshop on Frontiers in Handwriting Recognition, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00103854

O. Abdel-hamid, A. Mohamed, H. Jiang, and G. Penn, Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4277-4280, 2012.
DOI : 10.1109/ICASSP.2012.6288864

]. E. Petit, Grasp: Moteur de reconnaissance de gestes, 2010.