Abstract
Ambient Intelligence (AmI) is a new paradigm that specifically aims at exploiting sensory and context information in order to adapt the environment to the user’s preferences; one of its key features is the attempt to consider common devices as an integral part of the system in order to support users in carrying out their everyday life activities without affecting their normal behavior.
Our proposal consists in the definition of a gesture recognition module allowing users to interact as naturally as possible with the actuators available in a smart office, by controlling their operation mode and by querying them about their current state. To this end, readings obtained from a state-of-the-art motion sensor device are classified according to a supervised approach based on a probabilistic support vector machine, and fed into a stochastic syntactic classifier which will interpret them as the basic symbols of a probabilistic gesture language. We will show how this approach is suitable to cope with the intrinsic imprecision in source data, while still providing sufficient expressivity and ease of use.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aarts, E., Marzano, S.: The new everyday: views on ambient intelligence. 010 Publishers (2003)
De Paola, A., Gaglio, S., Lo Re, G., Ortolani, M.: Sensor9k: A testbed for designing and experimenting with WSN-based ambient intelligence applications. Pervasive and Mobile Computing 8(3), 448–466 (2012)
Suarez, J., Murphy, R.: Hand gesture recognition with depth images: A review. In: 2012 IEEE RO-MAN, pp. 411–417 (September 2012)
Li, Y.: Hand gesture recognition using kinect. In: 2012 IEEE 3rd International Conference on Software Engineering and Service Science (ICSESS), pp. 196–199 (June 2012)
Ren, Z., Yuan, J., Zhang, Z.: Robust hand gesture recognition based on finger-earth mover’s distance with a commodity depth camera. In: Proceedings of the 19th ACM International Conference on Multimedia. MM 2011, pp. 1093–1096. ACM, New York (2011)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vision 40(2), 99–121 (2000)
Hopcroft, J.E., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation, 3/E. Pearson Education (2008)
Fu, K.-S., Albus, J.E.: Syntactic pattern recognition and applications, vol. 4. Prentice-Hall, Englewood Cliffs (1982)
Liberman, M.Y.: The trend towards statistical models in natural language processing. In: Natural Language and Speech, pp. 1–7. Springer (1991)
Lu, S.-Y., Fu, K.-S.: Stochastic error-correcting syntax analysis for recognition of noisy patterns. IEEE Transactions on Computers 100(12), 1268–1276 (1977)
Booth, T.L., Thompson, R.A.: Applying probability measures to abstract languages. IEEE Transactions on Computers 100(5), 442–450 (1973)
Kean, S., Hall, J., Perry, P.: Meet the Kinect: An Introduction to Programming Natural User Interfaces, 1st edn. Apress, Berkely (2011)
Borenstein, G.: Making Things See: 3D Vision With Kinect, Processing, Arduino, and MakerBot. Make: Books. O’Reilly Media, Incorporated (2012)
Cottone, P., Lo Re, G., Maida, G., Morana, M.: Motion sensors for activity recognition in an ambient-intelligence scenario. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 646–651 (2013)
Lo Re, G., Morana, M., Ortolani, M.: Improving user experience via motion sensors in an ambient intelligence scenario. In: PECCS 2013 - Proceedings of the 3rd International Conference on Pervasive Embedded Computing and Communication Systems, Barcelona, Spain, February 19-21, pp. 29–34 (2013)
De Paola, A., La Cascia, M., Lo Re, G., Morana, M., Ortolani, M.: User detection through multi-sensor fusion in an ami scenario. In: 2012 15th International Conference on Information Fusion (FUSION), pp. 2502–2509 (July 2012)
Keogh, E., Wei, L., Xi, X., Lee, S.-H., Vlachos, M.: Lb keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. In: Proceedings of the 32nd International Conference on Very Large Data Bases. VLDB 2006, pp. 882–893. VLDB Endowment (2006)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)
Duan, K.-B., Keerthi, S.S.: Which is the best multiclass SVM method? An empirical study. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 278–285. Springer, Heidelberg (2005)
Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, pp. 61–74. MIT Press (1999)
Fung, L.-W., Fu, K.-S.: Stochastic syntactic decoding for pattern classification. IEEE Transactions on Computers 100(6), 662–667 (1975)
Gonzalez, R.C., Thomason, M.G.: Syntactic pattern recognition: An introduction. Addison-Wesley Publishing Company, Reading (1978)
Morana, M., De Paola, A., Lo Re, G., Ortolani, M.: An Intelligent System for Energy Efficiency in a Complex of Buildings. In: Proc. of the 2nd IFIP Conference on Sustainable Internet and ICT for Sustainability (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Gaglio, S., Lo Re, G., Morana, M., Ortolani, M. (2013). Gesture Recognition for Improved User Experience in a Smart Environment. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds) AI*IA 2013: Advances in Artificial Intelligence. AI*IA 2013. Lecture Notes in Computer Science(), vol 8249. Springer, Cham. https://doi.org/10.1007/978-3-319-03524-6_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-03524-6_42
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03523-9
Online ISBN: 978-3-319-03524-6
eBook Packages: Computer ScienceComputer Science (R0)