Deep Learning Sign Language Recognition System Based on Wi-Fi CSI
| Autor: | Marwa R. M. Bastwesy, Mohamed T. Faheem Saidahmed, Nada M. El-Shennawy |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
Computer Networks and Communications Computer science business.industry Speech recognition Deep learning Sign language Computer Science Applications Human-Computer Interaction Artificial Intelligence Modeling and Simulation Signal Processing Recognition system Artificial intelligence business |
| Zdroj: | International Journal of Intelligent Systems and Applications. 12:33-45 |
| ISSN: | 2074-9058 2074-904X |
| DOI: | 10.5815/ijisa.2020.06.03 |
| Popis: | Many sensing gesture recognition systems based on Wi-Fi signals are introduced because of the commercial off-the-shelf Wi-Fi devices without any need for additional equipment. In this paper, a deep learning-based sign language recognition system is proposed. Wi-Fi CSI amplitude and phase information is used as input to the proposed model. The proposed model uses three types of deep learning: CNN, LSTM, and ABLSTM with a complete study of the impact of optimizers, the use of amplitude and phase of CSI, and preprocessing phase. Accuracy, F-score, Precision, and recall are used as performance metrics to evaluate the proposed model. The proposed model achieves 99.855%, 99.674%, 99.734%, and 93.84% average recognition accuracy for the lab, home, lab + home, and 5 different users in a lab environment, respectively. Experimental results show that the proposed model can effectively detect sign gestures in complex environments compared with some deep learning recognition models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|