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).

翻译：本文提出并开发了一个简易实验测试平台，旨在研究将射频传感技术与相关知识蒸馏理论相结合的新颖思路的可行性，以设计轻量级、近实时且精准的人体姿态监测系统。所提出的相关知识蒸馏框架将鲁棒的"教师"模型中的姿态知识传递并融合至参数化的"学生"模型，这一技术有望在实现精准姿态估计的同时保持模型轻量化。为验证其有效性，我们在集成软件定义无线电的实验装置中实现了用于逻辑蒸馏的相关知识蒸馏技术，并研究了射频-视觉信号的关联性。该相关知识蒸馏射频传感技术具有两种模式：以摄像头供能的教师分类网络与以软件定义无线电供能的学生分类网络。具体而言，我们的相关知识蒸馏模型通过蒸馏与融合知识库，训练了一个双分支教师-学生网络。随后将训练所得的相关知识蒸馏模型用于识别多模态关联，并反向指导学生分支学习。不同于简单聚合学习结果，相关知识蒸馏训练包含两个域——视觉图像与射频信号——间的多路并行变换。训练完成后，该模型无需使用视觉信号/视频帧，仅凭射频信号即可高效保护隐私，并利用两个不同神经网络的多模态关联逻辑实现姿态估计。