MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition

TL;DR

MORIC introduces a novel Wi-Fi CSI-based human activity recognition method that decomposes signals into Doppler velocities and employs a robust time series classifier, significantly improving real-world accuracy and generalization.

Contribution

The paper presents a new approach combining delay-Doppler decomposition with a random convolutional kernel classifier to enhance Wi-Fi-based HAR robustness and generalization.

Findings

Outperforms state-of-the-art in generalization accuracy

Effective in recognizing challenging gestures

Calibration samples improve accuracy significantly

Abstract

The newly established IEEE 802.11bf Task Group aims to amend the WLAN standard to support advanced sensing applications such as human activity recognition (HAR). Although studies have demonstrated the potential of sub-7 GHz Wi-Fi Channel State Information (CSI) for HAR, no method currently performs reliably in real-world scenarios. This work tackles the poor generalization of Wi-Fi-based HAR by introducing an innovative approach to extracting and utilizing movement-related representations, which makes it robust to noise and static environmental properties. This is achieved by transforming CSI signals into the delay profile space and decomposing them into various Doppler velocities, which serve as informative projections of a mobile point's velocity from different unknown random angles. To mitigate the impact of this randomness, MORIC is introduced as a novel time series classification model based on random convolutional kernels, designed to be invariant to the random order and repetition of input representations, thereby enabling robust Wi-Fi CSI-based activity classification. Experimental results on the collected dataset demonstrate that the proposed method outperforms state-of-the-art approaches in terms of generalization accuracy for hand motion recognition, particularly for challenging gestures. Furthermore, incorporating a small number of calibration samples leads to a significant improvement in accuracy, enhancing the practicality of the method for real-world deployment.