RFID-based 3D human pose tracking: A subject generalization approach

作     者：Chao Yang Xuyu Wang Shiwen Mao Chao Yang;Xuyu Wang;Shiwen Mao

作者机构：Dept.of Electrical and Computer EngineeringAuburn UniversityAuburnAL36849-5201USA Dept.of Computer ScienceCalifornia State UniversitySacramentoCA95819-6021USA 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2022年第8卷第3期

页      面：278-288页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081001[工学-通信与信息系统] 

基　　金：supported in part by the US National Science Foundation(NSF)under Grants ECCS-1923163 and CNS-2107190 through the Wireless Engineering Research and Education Center at Auburn University 

主　　题：Radio-frequency identification(RFID) Three-dimensional(3D)human pose tracking Cycle-consistent adversarial network Generalization 

摘      要：Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.