Abstract
Traditional finger-tracking methods using sensors, cameras, and other devices are limited by high costs, environmental sensitivity, and inadequate user privacy protection. To address these challenges, we propose ARFG, an attach-free RFID finger-tracking system based on Generative Adversarial Network (GAN). ARFG captures time-series reflection signal changes resulting from finger movements in front of RFID tag arrays. These signals are transformed into feature maps that serve as inputs for DS-GAN, a fully supervised classification model using a semi-supervised algorithm. ARFG achieves accurate recognition of finger traces, with soft thresholding used to overcome the challenge of limited dataset training of conventional GANs. Extensive experiments demonstrate ARFG’s high accuracy, with average accuracy of 94.69% and up to 97.50% in conditions with few samples, various traces, users, finger speeds, and surroundings, showcasing its robustness and cross environmental capabilities.
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Acknowledgements
This paper is supported by the 2021 Fujian Foreign Cooperation Project(No. 2021I0001): Research on Human Behavior Recognition Based on RFID and Deep Learning; 2021 Project of Xiamen University (No. 20213160A0474): Zijin International Digital Operation Platform Research and Consulting; State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing Key Laboratory of Process Automation in Mining & Metallurgy(No. BGRIMM-KZSKL-2022-14): Research and application of mine operator positioning based on RFID and deep learning; National Key R&D Program of China-Sub-project of Major Natural Disaster Monitoring, Early Warning and Prevention(No. 2020YFC1522604).
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Li, S. et al. (2023). ARFG: Attach-Free RFID Finger-Tracking with Few Samples Based on GAN. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_64
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