We propose a real-time 3D human pose estimation and motion analysis method termed RePose for rehabilitation training. It is capable of real-time monitoring and evaluation of patients'motion during rehabilitation, providing immediate feedback and guidance to assist patients in executing rehabilitation exercises correctly. Firstly, we introduce a unified pipeline for end-to-end real-time human pose estimation and motion analysis using RGB video input from multiple cameras which can be applied to the field of rehabilitation training. The pipeline can help to monitor and correct patients'actions, thus aiding them in regaining muscle strength and motor functions. Secondly, we propose a fast tracking method for medical rehabilitation scenarios with multiple-person interference, which requires less than 1ms for tracking for a single frame. Additionally, we modify SmoothNet for real-time posture estimation, effectively reducing pose estimation errors and restoring the patient's true motion state, making it visually smoother. Finally, we use Unity platform for real-time monitoring and evaluation of patients' motion during rehabilitation, and to display the muscle stress conditions to assist patients with their rehabilitation training.

翻译：本文提出一种名为RePose的实时三维人体姿态估计与运动分析方法，用于康复训练。该方法能够实时监测和评估患者在康复过程中的运动状态，提供即时反馈与指导，协助患者正确执行康复训练动作。首先，我们提出一种基于多相机RGB视频输入的端到端实时人体姿态估计与运动分析统一流程，可应用于康复训练领域。该流程有助于监测并纠正患者动作，从而辅助其恢复肌肉力量与运动功能。其次，我们针对多人干扰的医疗康复场景提出一种快速跟踪方法，单帧跟踪耗时低于1毫秒。此外，我们改进SmoothNet算法以实现实时姿态估计，有效降低姿态估计误差并还原患者真实运动状态，使运动轨迹在视觉上更为平滑。最后，我们利用Unity平台对康复过程中的患者运动进行实时监测与评估，并展示肌肉应力状态以辅助患者完成康复训练。