SpecRF-Posture: Exploring Specular Reflections for Human Posture Recognition

This paper presents SpecRF-Posture, a novel lowcost approach for accurate Human Posture Recognition (HPR) using Radio Frequency (RF) signals. SpecRF-Posture leverages S21 parameters within the WiFi-6E frequency range for classification. We obtain a dataset of $\mathbf{S 2 1}$ parameters for different postures by performing beamscanning through mechanical rotation of a horn transmitter aimed at a reflective surface that illuminates the space of interest. We determine the $\mathbf{S 2 1}$ parameters of the signals that are then reflected back from the space onto an omnidirectional receiver. Thus for each posture we attain the S21 parameters of each possible illumination direction of the space. Experimental results demonstrate that SpecRF-Posture achieves an accuracy of $99.17 \%$ in posture classification, highlighting its effectiveness. Additionally, an RF dataset was acquired using a software package for automatic data acquisition within the WiFi-6E frequency range, and both the dataset and the software package have been made publicly available.