Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/89807
Title: | American sign language recognition and training method with recurrent neural network | Authors: | Lee, CKM Ng, KKH Chen, CH Lau, HCW Chung, SY Tsoi, T |
Issue Date: | 1-Apr-2021 | Source: | Expert systems with applications, 1 Apr. 2021, v. 167, 114403 | 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. | Keywords: | American sign language Leap motion controller Learning application Sign recognition system |
Publisher: | Pergamon Press | Journal: | Expert systems with applications | ISSN: | 0957-4174 | EISSN: | 1873-6793 | DOI: | 10.1016/j.eswa.2020.114403 | Rights: | © 2020 Elsevier Ltd. All rights reserved. © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. The following publication Lee, C. K. M., Ng, K. K. H., Chen, C.-H., 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 is available at https://dx.doi.org/10.1016/j.eswa.2020.114403. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Lee_American_Sign_Language.pdf | Pre-Published version | 1.92 MB | Adobe PDF | View/Open |
Page views
78
Last Week
1
1
Last month
Citations as of Apr 28, 2024
Downloads
122
Citations as of Apr 28, 2024
SCOPUSTM
Citations
67
Citations as of Apr 26, 2024
WEB OF SCIENCETM
Citations
39
Citations as of May 2, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.