Abstract
Sign language has become a form of communication for people who have some type of hearing impairment, in order to relate to the world around him and perform daily activities including education, work performance and personal development. In the knowledge society, the development of new Technologies for Information and Communication (TIC), they constitute a teaching tool that changes the conditions for learning and communication as the world is digitized. Therefore, the development of an Ecuadorian sign language learning system based on the use of a gesture sensor and an artificial neural network multilayer feed-forward, implementing the Backpropagation algorithm, which takes advantage of the parallel property of decreasing the time required by a processor to distinguish the existing relationship between given patterns, represents a solution for improving the teaching-learning process.
The system consists of three main layers: The first is responsible for the acquisition of data through a gestural device; the second uses the library of the gesture sensor to perform the acquisition and storage of information of the hand and the position of the fingers. Finally, the output layer, once the training of the neuron is analyzed, generates the real-time recognition of sign language.
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References
Organización Mundial de la Salud, “Sordera y pérdida de la audición,” March 2018
Consejo Nacional para la Igualdad de Discapacidades, “Estadísticas de Discapacidad,” Consejo Nacional para la Igualdad de Discapacidades, Ecuador, Estadistico (2017)
Palomares Ruiz, A.: Liderazgo y empoderamiento docente, nuevos retos de la educación inclusiva en la sociedad del conocimiento (2016)
Järvelä, S., et al.: Enhancing socially shared regulation in collaborative learning groups: designing for CSCL regulation tools. Educ. Technol. Res. Dev. 63(1), 125–142 (2015)
Rivas, D., et al.: LeSigLa_EC: learning sign language of Ecuador. In: Huang, T.-C., Lau, R., Huang, Y.-M., Spaniol, M., Yuen, C.-H. (eds.) SETE 2017. LNCS, vol. 10676, pp. 170–179. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71084-6_19
Cadeñanes Garnica, J.J., Arrieta, M.A.G.: Augmented reality sign language teaching model for deaf children. In: Omatu, S., Bersini, H., Corchado, Juan M., Rodríguez, S., Pawlewski, P., Bucciarelli, E. (eds.) Distributed Computing and Artificial Intelligence, 11th International Conference. AISC, vol. 290, pp. 351–358. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07593-8_41
Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43(1), 1–54 (2015)
Pigou, L., Dieleman, S., Kindermans, P.-J., Schrauwen, B.: Sign language recognition using convolutional neural networks. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8925, pp. 572–578. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16178-5_40
Mendoza, G., Fernanda, J., Zapata Sarzosa, M.B.: Desarrollo de una red neuronal para la clasificación y reconocimiento del lenguaje de signos ecuatoriano (2018)
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)
Lewis, J.R., Utesch, B.S., Maher, D.E.: Measuring perceived usability: the SUS, UMUX-LITE, and AltUsability. Int. J. Hum.-Comput. Interact. 31(8), 496–505 (2015)
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Rivas, D. et al. (2019). Intelligent System for the Learning of Sign Language Based on Artificial Neural Networks. In: De Paolis, L., Bourdot, P. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2019. Lecture Notes in Computer Science(), vol 11614. Springer, Cham. https://doi.org/10.1007/978-3-030-25999-0_27
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