Abstract
This paper presents a new robust method for inertial MEM (MicroElectroMechanical systems) 3D gesture recognition. The linear acceleration and the angular velocity, respectively provided by the accelerometer 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 geometric approach using DTW (Dynamic Time Warping) and a statistical method based on HMM (Hidden Markov Model) from filtered and denoised MEM data. Experimental results on 22 individuals producing 14 gestures in the air show that the proposed approach outperforms classical 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 information gives better results that using a single inertial description.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A practical approach for writer-dependent symbol recognition using a writer-independent symbol recognizer. IEEE Trans. PAMI 29(11), 1917–1926 (2007)
Akl, A., Valaee, S.: Accelerometer-based gesture recognition via dynamic-time warping, affinity propagation, & compressive sensing. In: ICASSP (2010)
Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: Sequential deep learning for human action recognition. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 29–39. Springer, Heidelberg (2011)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Networks (18), 5–6 (2005)
Hoffman, M., Varcholik, P., LaViola, J.: Breaking the status quo: Improving 3d gesture recognition with spatially convenient input devices. In: Virtual Reality Conference (VR), pp. 59–66 (2010)
Hofmann, F.G., Heyer, P., Hommel, G.: Velocity profile based recognition of dynamic gestures with discrete hidden markov models. In: Wachsmuth, I., Fröhlich, M. (eds.) GW 1997. LNCS (LNAI), vol. 1371, pp. 81–95. Springer, Heidelberg (1998)
Kallio, S., Kela, J., Mantyjarvi, J.: Online gesture recognition system for mobile interaction. Systems, Man and Cybernetics 3, 2070–2076 (2003)
Kela, J., Korpipää, P., Mäntyjärvi, J., Kallio, S., Savino, G., Jozzo, L., Marca, D.: Accelerometer-based gesture control for a design environment. Personal Ubiquitous Comput. 10(5), 285–299 (2006)
Liu, J., Wang, Z., Zhong, L., Wickramasuriya, J., Vasudevan, V.: uwave: Accelerometer-based personalized gesture recognition and its applications. In: IEEE PerCom, pp. 1–9 (2009)
Petit, E.: GRASP: Moteur de reconnaissance de gestes. Technical report, France Télécom R&D (2007)
Pylvänäinen, T.: Accelerometer Based Gesture Recognition Using Continuous HMMs. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 639–646. Springer, Heidelberg (2005)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation (9), 1735–1780 (1997)
Wilson, D.H., Wilson, A.: Gesture recognition using the xwand. Technical Report CMU-RI-TR-04-57, Robotics Institute (April 2004)
Wu, J., Pan, G., Zhang, D., Qi, G., Li, S.: Gesture recognition with a 3-D accelerometer. In: Zhang, D., Portmann, M., Tan, A.-H., Indulska, J. (eds.) UIC 2009. LNCS, vol. 5585, pp. 25–38. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lefebvre, G., Berlemont, S., Mamalet, F., Garcia, C. (2013). BLSTM-RNN Based 3D Gesture Classification. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-40728-4_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40727-7
Online ISBN: 978-3-642-40728-4
eBook Packages: Computer ScienceComputer Science (R0)