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A Gesture Recognition Design Toolkit Everyday Gesture Library
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 121 - Number 14 |
| Year of Publication: 2015 |
| Authors: Muhammad Haroon, Malik Sikandar Hayat Khiyal |
10.5120/21609-4722
|
Muhammad Haroon, Malik Sikandar Hayat Khiyal . A Gesture Recognition Design Toolkit Everyday Gesture Library. International Journal of Computer Applications. 121, 14 ( July 2015), 25-29. DOI=10.5120/21609-4722
Abstract
Creating gestural recognition system is a challenging task which requires skills and updated applications. It is required for designer to be skillful and innovative in order to create interesting and acceptable gestures. Gesture Recognition Design Toolkit (GRDT) is set of tools designed to simplify the gesture creation process for non experts. From designing a new gesture, to suggesting the best fit, to test it for false positive activation, till final selection, are the step-by-step functions of Gesture Recognition Design Toolkit. Creating gestural recognition system, interface designers encounter many challenges throughout the process. Selecting the best gesture for a particular motion with low probability of false positives is one of many. This study explores ways to improve the functionality and enhance the efficiency of gestural library using machine learning techniques and data mining
References
- Rubine, D. H: The Automatic Recognition of Gestures. Ph. D. Thesis. Carnegie Melon University, CMU-CS-91-202 (1991).
- Kohlsdorf, D. , and Starner, T: MAGIC Summoning: Towards Automatic Suggesting and Testing of Gestures with Low Probability of False Positives during Use. Journal of Machine Learning Research 14: 209-242 (2013).
- Ashbrook, D. , and T Starner: MAGIC: a motion gesture design tool. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pages 2159–2168, 2010.
- Fails, J. A. , and Olsen, D,R: A Design tool for camera- based interaction. In proceeding of the ACM conference on human factor in computing systems (CHI), pages 449-456, 2003.
- Edwin, H. , James, H. , and Donald, N. : 1985 Direct manipulation interfaces. Human-Computer Interaction, volume 1, pp. 311–338.
- Felferning,A. , Burke, R. , and P Pu: Preface to the special issue on user interfaces for recommender systems. Journal of User Modeling and User-Adapted Interaction 22: 313-316 (2012).
- Gillian, N. , and Paradiso, J. A: The gesture recognition toolkit. Journal of Machine Learning Research 15: 3483-3487 (2014).
- Kohlsdorf, D. Starner, T. , and Ashbrook, D. , 2011 MAGIC 2. 0: A web tool for false positive prediction and prevention for gesture recognition systems. In Proceedings of the International Conference on Automatic Face and Gesture Recognition, pages 1–6, Washington, District of Columbia.
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