Robust Multi-User Pose Estimation Based on Spatial and Temporal Features from WiFi CSI

Pose estimation based on WiFi channel state information (CSI) has gained popularity in recent years. Most existing works, however, focus only on scenarios with a single user. For those research endeavors that can handle multi-user scenarios, on the other hand, they suffer from the robustness problem against environment changes due to their reliance on the use of domain-specific features such as CSI amplitude and phase. In this paper, we propose to use both spatial and temporal features for multi-user pose estimation that can show resilience to environment changes. The features we use combine the angle of arrival (AoA), time of flight (ToF), and Doppler shift for capturing the location and motion information of multiple users. Unlike existing feature extraction methods that only take CSI either along the time axis or across different frequency subcarriers, we propose new methods to jointly estimate AoA-ToF and AoA-Doppler heatmaps from consecutive CSI samples and subcarriers for increasing the resolutions of the desired features. An experiment testbed is set up to evaluate the performance of the proposed system. Results show that our system outperforms the existing model by 12% in precision across different scenarios with changes in the environment, thus substantiating the benefits of using both spatial and temporal features for multi-user pose estimation.