RF-Dial: Rigid Motion Tracking and Touch Gesture Detection for Interaction via RFID Tags

Bu, Yanling; Xie, Lihua; Gong, Yinyin; Wang, Chuyu; Yang, Lei; Liu, Jia; Lu, Sanglu

doi:10.1109/tmc.2020.3017721

Cited by 24 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RFID, which is used in various fields, transmits signals from a signal transmitter to irradiate the target action and uses the phase and RSSI characteristics of the reflected signals to identify it because of this characteristic. Most previous studies have demonstrated the use of RFID in the fields of location tracking [6], [35], activity recognition [26], [27], [28], [36], health monitoring [37], privacy protection [5], and so on. For example, in the case of POLO, a mobile robot was operated to localize tagged items using NRP algorithms, with an array of tags carried by itself [6].…”

Section: B Rfid-based Sensingmentioning

confidence: 99%

RF-CSign: A Chinese Sign Language Recognition System Based on Large Kernel Convolution and Normalization-Based Attention

Xu,

Zhang,

Yang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Deaf people use sign language for communication, which relies on the movement gestures of body parts and plays a vital role in human-computer interaction. Most wireless sensing-based gesture recognition studies have recognized simple gestures but overlooked the recognition of complex activities, such as sign language. In addition, cross-domain recognition often requires a large amount of data to train classifiers for each environment. Therefore, we propose RF-CSign, which aims to achieve high accuracy in sign language recognition and cross-domain recognition. First, we use Radio Frequency Identification (RFID) to collect signals and obtain denoised signals through data pre-processing, so that they can be processed in a neural network. Second, the RF-CSign network is proposed with the inclusion of large kernel convolution to reduce the complexity of the model and to make the model with long-range correlations, thereby enhancing recognition accuracy. Third, RF-CSign employs a pixel Normalization-based Attention Module (NAM) to enhance the stability of the model, thereby addressing the problem of model overfitting. Finally, RF-CSign achieves high accuracy in cross-domain environments through a migration learning approach. The experimental results showed that the average recognition accuracy of RF-CSign reached 99.17%, and the average recognition accuracy for new users and new environments recorded 96.67% and 97.50%, respectively.

show abstract