SentenceLevel Sign Language Recognition Using RF signals Sign language recognition is emerging as a vital component of our smart life. In addition, commercial RFID shall become a popular technology for sign language recognition. As we all know, there are 70 million deaf people using sign language as their first language and sign language can facilitate communication with deaf people. However, most of the researches are isolated word recognition. There is few researches about sentencelevel sign language recognition. More importantly, they are limited and it is difficult to achieve the desired results of realworld applications. So this paper introduces the first sentencelevel sign language recognition system based on RFID. It mainly collects the phase sequence of signals received by commercial RFID device. We obtain relatively pure phase characteristics and present a method to carry out sign language segmentation. Effective feature extraction and classifier selection are crucial to sign language recognition. By evaluating our system in realword environment, we fill in the gaps between corresponding lowcost sentencelevel sign language recognition. We implement and evaluate through extensive experiments and the average accuracy of the method are 96% and 98.11% in different multipath scenarios. The results show that our method has high recognition accuracy and robustness.

A Bidirectional Interactive System of Sign Language and Visual Speech Based on Portable Devices At present, the natural communication between deaf and normal people is a major challenge both in theoretical research and application. In addition, in the field of HumanMachine Interaction, there is little work on bidirectional communication based on sign language and visual speech. In this paper, a portable bidirectional interactive system of sign language and visual speech was proposed to help deaf people communicate naturally under certain circumstances. In the section of modeling, Network in Network model was employed to classify sign language words, while the method using a network combined DenseNet and LSTM together was used for lip reading. By wearing the portable device, people can achieve bidirectional translation and communication of visual speech and sign language. We built our own Chinese sign language database containing more than 100 categories of words and conducted experiments in the context of the airport for experimental verification. Eventually, the average time of each round of conversation is within 15 seconds, faster than normal communication without system assistance, which verified the effectiveness and advantage of the purposed system.