Automatic Classification of Polish Sign Language Words

In the article we present the approach to automatic recognition of hand gestures using eGlove device. We present the research results of the system for detection and classification of static and dynamic words of Polish language. The results indicate the usage of eGlove allows to gain good recognition quality that additionally can be improved using additional data sources such as RGB cameras. Streszczenie. W artykule przedstawiono podejście do automatycznego rozpoznawania gestow migowych w oparciu o dedykowane do tego zadania urządzenie pod nazwą eGlove. Przeprowadzono analize podejśc do klasyfikacji gestow statycznych i dynamicznych. Uzyskane rezultaty wskazują, ze opracowane urządzenie moze zostac wykorzystane do analizy gestow jezyka mowionego, jednakze dla gestow dynamicznych ograniczeniem jest rozmiar slownika. (Automatyczna klasyfikacja znakow Polskiego Jezyka Miganego).

[1]  Rafael Bidarra,et al.  Generating Consistent Buildings: A Semantic Approach for Integrating Procedural Techniques , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[2]  Gideon Steinberg Natural User Interfaces , 2012 .

[3]  Ernst Kruijff,et al.  Unconventional human computer interfaces , 2004, SIGGRAPH '04.

[4]  Gregory D. Abowd,et al.  Human-Computer Interaction (3rd Edition) , 2003 .

[5]  Robert W. Lindeman,et al.  A multi-class pattern recognition system for practical finger spelling translation , 2002, Proceedings. Fourth IEEE International Conference on Multimodal Interfaces.

[6]  M. Weiser,et al.  Hot topics-ubiquitous computing , 1993 .

[7]  Andrew T. Duchowski,et al.  Eye Tracking Methodology: Theory and Practice , 2003, Springer London.

[8]  Christian Kothe,et al.  Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general , 2011, Journal of neural engineering.

[9]  D Roggen,et al.  Wearable Computing , 2011, IEEE Robotics & Automation Magazine.

[10]  Liviu Goras,et al.  On HMM static hand gesture recognition , 2011, ISSCS 2011 - International Symposium on Signals, Circuits and Systems.

[11]  Joanna Marnik The Polish Finger Alphabet Hand Postures Recognition Using Elastic Graph Matching , 2008, Computer Recognition Systems 2.

[12]  Hiroshi Ishii,et al.  Tangible interfaces for remote collaboration and communication , 1998, CSCW '98.

[13]  Zhen Wang,et al.  uWave: Accelerometer-based Personalized Gesture Recognition and Its Applications , 2009, PerCom.

[14]  John Krumm,et al.  Ubiquitous Computing Fundamentals , 2009 .

[15]  Emiko Charbonneau,et al.  The Wiimote and Beyond: Spatially Convenient Devices for 3D User Interfaces , 2010, IEEE Computer Graphics and Applications.