SpecRF-Posture Dataset: Exploring Specular Reflections for Human Posture Recognition

In recent years, the utilization of Radio Frequency (RF) signals for Human Posture Recognition (HPR) has emerged as a promising approach in wireless sensing technology. Recent studies within RF systems have demonstrated the effectiveness of S21 parameters for human body-related classification tasks. Inspired by these advancements and the enhanced network performance offered by WiFi-6E, our system leverages S21 parameters within this frequency range for posture recognition. By analyzing the S21 parameters, we introduce SpecRF-Posture, a novel system designed for accurate classification of human posture. Unlike previous approaches, we explore the use of low-cost hardware, based only on passive specular reflections that occur in the path between a steerable horn transmitter, a reflective surface, the Space-of-Interest (SoI), and an omnidirectional receiver. To characterize the region where the individual is positioned using specular reflections, our system achieves beam scanning by mechanically rotating the transmitter at regular intervals. Our work evaluates the viability of utilizing passive scatters within the propagation medium for HPR or similar tasks, at a low hardware cost. The data collected during this work is here published as a public dataset so that the community can access it and test different classification algorithms in order to evaluate them on this task.