Please use this identifier to cite or link to this item:
|Title:||American sign language recognition and training method with recurrent neural network||Authors:||Lee, C. K. M.
Ng, Kam K. H.
Lau, H. C. W.
Chung, S. Y.
|Keywords:||Engineering::Mechanical engineering||Issue Date:||2021||Source:||Lee, C. K. M., Ng, K. K. H., Chen, C., Lau, H. C. W., Chung, S. Y. & Tsoi, T. (2021). American sign language recognition and training method with recurrent neural network. Expert Systems With Applications, 167, 114403-. https://dx.doi.org/10.1016/j.eswa.2020.114403||Journal:||Expert Systems with Applications||Abstract:||Though American sign language (ASL) has gained recognition from the American society, few ASL applications have been developed with educational purposes. Those designed with real-time sign recognition systems are also lacking. Leap motion controller facilitates the real-time and accurate recognition of ASL signs. It allows an opportunity for designing a learning application with a real-time sign recognition system that seeks to improve the effectiveness of ASL learning. The project proposes an ASL learning application prototype. The application would be a whack-a-mole game with a real-time sign recognition system embedded. Since both static and dynamic signs (J, Z) exist in ASL alphabets, Long-Short Term Memory Recurrent Neural Network with k-Nearest-Neighbour method is adopted as the classification method is based on handling of sequences of input. Characteristics such as sphere radius, angles between fingers and distance between finger positions are extracted as input for the classification model. The model is trained with 2600 samples, 100 samples taken for each alphabet. The experimental results revealed that the recognition rate for 26 ASL alphabets yields an average of 99.44% accuracy rate and 91.82% in 5-fold cross-validation with the use of leap motion controller.||URI:||https://hdl.handle.net/10356/160679||ISSN:||0957-4174||DOI:||10.1016/j.eswa.2020.114403||Schools:||School of Mechanical and Aerospace Engineering||Rights:||© 2020 Elsevier Ltd. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||MAE Journal Articles|
Updated on Nov 27, 2023
Web of ScienceTM
Updated on Oct 25, 2023
Updated on Dec 4, 2023
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.