A sensing data and deep learning-based sign language recognition approach

Correct sign language recognition helps deaf people communicate normally with hearing people. However, existing sign language recognition techniques have low recognition accuracy due to insufficient feature extraction. In this paper, we propose an approach to improve the accuracy of sign language recognition. Firstly, we design a one-dimensional convolutional neural network (CNN) using the skip connection approach Secondly, we propose an improved multi-head attention mechanism that incorporates bi-directional long short-term memory (BiLSTM) networks within a multi-head attention mechanism. Thirdly, we position this improved multi-head attention mechanism behind the final convolutional layer of the proposed CNN architecture and denote the resultant architecture as BMCNN. Finally, we verify the performance of our approach with BMCNN architecture through ten-fold cross-validation on the sensing dataset. Our approach achieves 99.13% accuracy in sign language recognition. The results show that our proposed approach outperforms the traditional machine learning approaches and other state-of-the-art techniques in this field.