Evaluating the Impact of CSI Preprocessing on WiFi-Based Human Activity Recognition

Abstract

Wi-Fi Channel State Information (CSI) is a robust, privacy-preserving modality for Human Activity Recognition (HAR). Since raw CSI suffers from hardware desynchronizations and noise, preprocessing is vital. This study conducts an empirical ablation of CSI preprocessing using a fixed Two-Stream 2D CNN(Convolutional Neural Network) to quantify its impact on classification accuracy and latency. Results reveal that preprocessing, apart from architectural complexity, is the primary driver of the accuracy-latency trade-off. Computationally heavy methods like Hampel filtering introduce massive latency (>162 ms) without accuracy gains. In contrast, lightweight frequency-domain filtering consistently yields superior results. Specifically, dual-stream Butterworth bandpass filtering achieves 96.43% accuracy with only 49.08 ms latency. These findings demonstrate that isolating motion-relevant frequencies enables efficient, high-performance HAR suitable for real-time edge deployment.