Alternating Electric FieldBased Static GestureRecognition Technology Currently, gesture recognition based on electricfield detection technology has received extensive attention, which is mostly used to recognize the position and the movement of the hand, and rarely used for identification of specific gestures. A noncontact gesturerecognition technology based on the alternating electricfield detection scheme is proposed, which can recognize static gestures in different states and dynamic gestures. The influence of the hand on the detection system is analyzed from the principle of electricfield detection. A simulation model of the system is established to investigate the charge density on the hand surface and the potential change of the sensing electrodes. According to the simulation results, the system structure is improved, and the signalprocessing circuit is designed to collect the signal of sensing electrodes. By collecting a large amount of data from different operators, the treemodel recognition algorithm is designed and a gesturerecognition experiment is implemented. The results show that the gesturerecognition correct rate is over 90%. With advantages of high response speed, low cost, small volume, and immunity to the surrounding environment, the system could be assembled on a robot that communicates with operators.

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.