LoDiHAR: A Low-Cost Distributed Human Activity Recognition System Based on RFID

Human Activity Recognition has been extensively applied to fulfill tasks such as fall detection, human-computer interaction, virtual reality, etc. Existing radio frequency-based HAR methods, although overcoming limitations of wearable-, visual-, and acoustic-based sensing technology, still suffer from high costs and low efficiency, which limits their pervasive use. In this paper, we propose LoDiHAR, a low-cost, distributed HAR system leveraging Radio Frequency Identification technology. LoDiHAR employs low-cost and fully programmable commercial wireless components, providing full access to the PHY samples of the backscattered signals, in which signal phases can be extracted to infer different activities. Different from COTS RFID systems that adopt a polling interrogation scheme, LoDiHAR supports a distributed sensing scheme, which profiles human activities more efficiently. LoDiHAR addresses a series of technical challenges such as accurate phase extraction from backscattered signals, asynchronous distributed RF data fusion and insufficient training data. A Conditional Generative Adversarial Network framework combined with a Transformer model is designed for accurate time-series activity classification. LoDiHAR demonstrates pro-ficiency in recognizing eight types of human activities across diverse environments, achieving an accuracy of up to 94.9% while only costing 10% of the mainstream COTS RFID systems.