  In this work, we propose and develop a simple experimental testbed to study
the feasibility of a novel idea by coupling radio frequency (RF) sensing
technology with Correlated Knowledge Distillation (CKD) theory towards
designing lightweight, near real-time and precise human pose monitoring
systems. The proposed CKD framework transfers and fuses pose knowledge from a
robust "Teacher" model to a parameterized "Student" model, which can be a
promising technique for obtaining accurate yet lightweight pose estimates. To
assure its efficacy, we implemented CKD for distilling logits in our integrated
Software Defined Radio (SDR)-based experimental setup and investigated the
RF-visual signal correlation. Our CKD-RF sensing technique is characterized by
two modes - a camera-fed Teacher Class Network (e.g., images, videos) with an
SDR-fed Student Class Network (e.g., RF signals). Specifically, our CKD model
trains a dual multi-branch teacher and student network by distilling and fusing
knowledge bases. The resulting CKD models are then subsequently used to
identify the multimodal correlation and teach the student branch in reverse.
Instead of simply aggregating their learnings, CKD training comprised multiple
parallel transformations with the two domains, i.e., visual images and RF
signals. Once trained, our CKD model can efficiently preserve privacy and
utilize the multimodal correlated logits from the two different neural networks
for estimating poses without using visual signals/video frames (by using only
the RF signals).
