Gesture recognition can help people with a speech impairment to communicate and promote the development of Human-Computer Interaction (HCI) technology. With the development of wireless technology, passive gesture recognition based on RFID has become a research hotspot. In this paper, we propose a low-cost, non-invasive and scalable gesture recognition technology, and successfully implement the RF-alphabet, a gesture recognition system for complex, fine-grained, domain-independent 26 English letters; the RF-alphabet has three major advantages: first, this paper achieves complete capture of complex, fine-grained gesture data by designing a dual-tag, dual-antenna layout. Secondly, to overcome the disadvantages of the large training sets and long training times of traditional deep learning. We design and combine the Difference threshold similarity calculation prediction model to extract digital signal features to achieve real-time feature analysis of gesture signals. Finally, the RF alphabet solves the problem of confusing the signal characteristics of letters. Confused letters are distinguished by comparing the phase values of feature points. The RF-alphabet ends up with an average accuracy of 90.28% and 89.7% in different domains for new users and new environments, respectively, by performing feature analysis on similar signals. The real-time, robustness, and scalability of the RF-alphabet are proven.

Recently, radio frequency identification (RFID) sensing has attracted much attention due to its contact-free nature, low cost, light weight and other advantages. RFID-based person detection has also become a hot research topic, but there are still some problems in the existing research. First, most of the current studies cannot identify numerous people at a time well. Second, in order to detect more accurately, it is necessary to evaluate the whole-body activity of a person, which will consume a lot of time to process the data and cannot be applied in time. To solve these problems, in this paper we propose RF-Detection, a person detection system using RFID. First of all, RF-Detection takes step length as the standard for person detection, divides step length into specific sections according to the relationship between step length and height, and achieves high accuracy for new user detection through a large amount of training for a specific step length. Secondly, RF-Detection can better identify the number of people in the same space by segmenting continuous people. Finally, the data collection was reduced by expanding the data set, and the deep learning method was used to further improve the accuracy. The results show that the overall recognition accuracy of RF-Detection is 98.93%.

Gesture recognition, the basis of human–computer interaction (HCI), is a significant component for the development of smart home, VR, and senior care management. Most gesture recognition methods still depend on sensors worn by the user or video-based gestures for recognition, can be used for fine-grained gesture recognition. our paper implements a gesture recognition method that is independent of environment and gesture drawing direction, and it achieves gesture recognition classification by using small sample data. Wi-NN, proposed in this study, does not require the user to wear additional device. In this case, channel state information (CSI) extracted from Wi-Fi signal is used to capture the action information of the human body via CSI. After pre-processing to reduce the interference of environmental noise as much as possible, clear action information is extracted using the feature extraction method based on time domain to obtain the gesture action feature data. The gathered data are integrated with the weighted k-nearest neighbor (KNN) classification recognizer for classification task. The experiment outcomes revealed that the accuracy scores of the same gesture for different users and different gestures for the same user under the same environment were 93.1% and 89.6%, respectively. The experiments in different environments also achieved good recognition results, and by comparing with other experimental methods, the experiments in this paper have better recognition results. Evidently, good classification results were generated after the original data were processed and incorporated into the weighted KNN.