Abstract
The nonverbal communication can be informally defined as the communicative process between two or more entities (e.g., persons) which achieving an informative exchange without using the semantic meaning of the words. This process can be accomplished by using one or more language forms, including the body language (i.e., movements, gestures, and postures) which in turn can be composed by voluntary and involuntary behaviours. The analysis and interpretation of these behaviours can infer different internal states of persons (e.g., feelings, attitudes, emotions) which in turn can support the development of a wide range of automatic applications in different fields, such as: rehabilitation, security, people identification, human behaviour analysis, biometric.
In recent years, we have focused our efforts in developing a first implementation of Kinematic, a novel multimodal framework designed to support advanced human-machine interfaces. The purpose of the framework is to provide a tool to analyze and interpret verbal and nonverbal human-to-human communication in order to transfer this ability to the human-machine interaction. In this paper we face a specific aspect of the framework regarding the first calibration phase of the numerical measures related to the Kinect skeleton used to analyze and interpret the body language. The numerical measures was obtained analyzing the movements of the skeleton during individual and social contexts. A preliminary qualitative and quantitative study has been reported and discussed.
Chapter PDF
Similar content being viewed by others
References
Avola, D., Bottoni, P., Dafinei, A., Labella, A.: Color-based recognition of gesture-traced 2d symbols. In: Proceedings of the 17th International Conference on Distributed Multimedia Systems, DMS 2011, pp. 5–6. Knowledge Systems Institute, Convitto della Calza (2011), http://dblp.uni-trier.de
Avola, D., Del Buono, A., Del Nostro, P., Wang, R.: A novel online textual/Graphical domain separation approach for sketch-based interfaces. In: Damiani, E., Jeong, J., Howlett, R.J., Jain, L.C. (eds.) New Directions in Intelligent Interactive Multimedia Systems and Services - 2. SCI, vol. 226, pp. 167–176. Springer, Heidelberg (2009), http://dx.doi.org/10.1007/978-3-642-02937-0_15
Avola, D., Cinque, L., Levialdi, S., Placidi, G.: Kinematic: A kinect based framework to support advanced human-machine interfaces - ver. i. In: Internal Technical Report in Human-Computer Interfaces, ITR-HCI 2012, pp. 1–85. Sapienza University and Univeristy of L’Aquila (2012), http://dblp.uni-trier.de
Avola, D., Cinque, L., Levialdi, S., Placidi, G.: Kinematic: A kinect based framework to support advanced human-machine interfaces - ver. ii. In: Internal Technical Report in Human-Computer Interfaces, ITR-HCI 2013, pp. 1–158. Sapienza University and Univeristy of L’Aquila (2013), http://dblp.uni-trier.de
Bhat, A., Hammond, T.: Using entropy to distinguish shape versus text in hand-drawn diagrams. In: Proceedings of the 21st International Jont Conference on Artifical Intelligence, IJCAI 2009, pp. 1395–1400. Morgan K. Publishers Inc., USA (2009), http://dl.acm.org/citation.cfm?id=1661445.1661669
Champa, H., AnandaKumar, K.: Automated human behavior prediction through handwriting analysis. Integrated Intelligent Computing 1(1), 160–165 (2010)
Charbonnier, M.: The understanding of nonverbal communication in bilingual children. Ph.D. Thesis in Developmental and Social Psychology, XIX Cycle, pp. 1–126. University of Padova, Padova (2008), http://paduaresearch.cab.unipd.it/290/
Coll, R., Fornes, A., Llados, J.: Graphological analysis of handwritten text documents for human resources recruitment. In: International Conference on Document Analysis and Recognition, vol. 2(3), pp. 1081–1085 (2009)
Fonseca, M., Pimentel, C., Jorge, J.: Cali: An online scribble recognizer for calligraphic interfaces. In: AAAI 2002 Spring Symposium, AAAI 2002, pp. 51–58 (2002), http://dx.doi.org/10.1007/978-1-84882-812-4_7
Fonseca, M.J., Jorge, J.A.: Experimental evaluation of an on-line scribble recognizer. Pattern Recognition Letters 22(12), 1311–1319 (2001), http://www.sciencedirect.com/science/article/pii/S0167865501000769
Grice, H.: Meaning. Philosophical Review 66(1), 377–388 (1957), http://www.sciencedirect.com/science/article/pii/S0167865501000769
Jakobson, R.: Language in Literature, 2nd edn. Belknap Press Series. Harvard University Press (1987)
Kala, R., Vazirani, H., Shukla, A., Tiwari, R.: Offline handwriting recognition using genetic algorithm. International Journal of Computer Science Issues 7(1), 16–25 (2010)
Kimura, F., Shridhar, M.: Handwritten numerical recognition based on multiple algorithms. Pattern Recogn. 24(10), 969–983 (1991), http://dx.doi.org/10.1016/0031-32039190094-L
Kinect (2013), http://www.microsoft.com/en-us/kinectforwindows/
Shannon, C., Weaver, W.: The Mathematical Theory of Communication, Illini Books Edn., vol. 1. University of Illinois Press (1949)
Skeleton (2013), http://microsoft.com/library/microsoft.kinect.jointtype.aspx
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
Avola, D., Cinque, L., Levialdi, S., Placidi, G. (2013). Human Body Language Analysis: A Preliminary Study Based on Kinect Skeleton Tracking. In: Petrosino, A., Maddalena, L., Pala, P. (eds) New Trends in Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41190-8_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-41190-8_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41189-2
Online ISBN: 978-3-642-41190-8
eBook Packages: Computer ScienceComputer Science (R0)