Abstract
Several million people around the world use signs as their main mean of communication. The advances in technologies to recognize such signs will make possible the computer supported interpretation of sign languages. There are more than 137 different sign language around the world; therefore, a system that interprets those languages could be beneficial to all, including the Deaf Community. This paper presents a system based on a hand tracking device called Leap Motion, used for signs recognition. The system uses a Support Vector Machine for sign classification. We performed three different evaluations of our system with over 24 people.
Keywords
- American Sign Language
- Leap Motion
- Support Vector Machine
- Automatic sign language recognition
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lewis, P., Simons, G., Fennig, C.: Ethnologue: Languages of the World. SIL International, Dallas (2009)
Hanson, V.: Computing technologies for deaf and hard of hearing users. In: Sears, A., Jacko, J. (eds.) Human Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, pp. 886–893. CRC Press, Boca Raton (2012)
Leap motion. http://www.leapmotion.com
Guna, J., Jakus, G., Pogačnik, M., Tomažič, S., Sodnik, J.: An analysis of the precision and reliability of the leap motion sensor and its suitability for static and dynamic tracking. Sensors 14(2), 3702–3720 (2014)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Caridakis, G., Asteriadis, S., Karpouzis, K.: Non-manual cues in automatic sign language recognition. Pers. Ubiquit. Comput. 18(1), 37–46 (2014)
Zafrulla, Z., Brashear, H., Starner, T., Hamilton, H., Presti, P.: American Sign Language recognition with the kinect. In: Proceedings of the 13th International Conference on Multimodal Interfaces, pp. 279–286. ACM, New York (2011)
Sun, C., Zhang, T., Xu, C.: Latent support vector machine modeling for sign language recognition with kinect. ACM Trans. Intell. Syst. Technol. 6(2), 1–20 (2015)
Potter, L., Araullo, J., Carter, L.: The leap motion controller: a view on sign language. In: Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration, pp. 175–178. ACM. New York (2013)
Chuan, C., Regina, E., Guardino, C.: American Sign Language recognition using leap motion sensor. In: International Conference on Machine Learning and Applications, pp. 541–544. IEEE Press, New York (2014)
Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. Neurocomputing 68, 41–50 (1990)
Scikit-learn: machine learning in python. http://scikit-learn.org/
Mohandes, M., Aliyu, S., Deriche, M.: Arabic sign language recognition using the leap motion controller. In: IEEE International Symposium on Industrial Electronics, pp. 960–965. IEEE Press, New York (2014)
Acknowledgments
This work was partially supported by the Escuela de Ciencias de la Computación e Informática at Universidad de Costa Rica (ECCI-UCR) grand No. 320-B5-291, by Centro de Investigaciones en Tecnologías de la Información y Comunicación de la Universidad de Costa Rica (CITIC-UCR), and by Ministerio de Ciencia, Tecnología y Telecomunicaciones (MICITT) and Consejo Nacional para Investigaciones Científicas y Tecnológicas (CONICIT) of the Government of Costa Rica.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
This appendix shows the SVM classification potential by researcher (“sometimes” category) and the user evaluation results (each sign was performed 48 times). The selected signs for the user evaluation were detailed in Fig. 3.
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Quesada, L., López, G., Guerrero, L.A. (2015). Sign Language Recognition Using Leap Motion. In: García-Chamizo, J., Fortino, G., Ochoa, S. (eds) Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information. UCAmI 2015. Lecture Notes in Computer Science(), vol 9454. Springer, Cham. https://doi.org/10.1007/978-3-319-26401-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-26401-1_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26400-4
Online ISBN: 978-3-319-26401-1
eBook Packages: Computer ScienceComputer Science (R0)