Human Activity Recognition Using Wi-Fi Signals based on Tokenized Signals with Attention

In this paper, we construct a network for human activity recognition based on the tokenized Wi-Fi signals on an attention mechanism. After standardizing the signals, the WiFi channel state information is utilized as a set of time-series data, acknowledging its inherent temporal structure. Motivated by the Transformer’s ability to model temporal dependencies, the construction is enriched with a frequency-based tokenization scheme. This unique construction is adept at managing noise and sensitivity intrinsic to Wi-Fi signals, effectively mitigating the challenges in Wi-Fi-based human activity recognition. Our experimental evaluations validated the effectiveness of the proposed structure.