الفهرس 
Introduction
Signal pre-processingOnly 14 pages are availabe for public view
 |  | from | 118 |  |  |
| | | from | 118 | | |
Abstract
It is known that there are research sensing gesture recognition
techniques 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 thesis, we use the well known public American Sign Language
(ASL) which based on Channel State Information (CSI) dataset collected
from different environments. To achieve such words, a deep learning-based
sign language recognition system is proposed. In order to build a unique
pattern for each sign word, we use the Wi-Fi CSI amplitude and phase
information 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 of 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. Also, a new sign
language recognition system which includes the attention mechanism with
the convolutional neural network and bidirectional Long-Short-Term-Memory (CNN-BiLSTM) is proposed. It achieves 95.643%, 98.025%,
98.804% and 91.12% recognition accuracy for the home, lab, lab + home,
and 5 different users in a lab environment, respectively.