用AI生成个性化康复视频并实时纠正动作，提升居家康复依从性。
Agentic AI for Personalized Physiotherapy: A Multi-Agent Framework for Generative Video Training and Real-Time Pose Correction
- 四类智能体协同：解析病历、生成定制视频、实时识姿、给出纠正建议。
- 基于大模型与MediaPipe构建原型，可动态适配患者伤情与居家环境。
- 适合远程康复、个性化医疗及智能健康设备开发者参考。
居家康复依从性低，主要因缺乏个性化监督与动态反馈。现有数字健康方案依赖静态视频库或通用3D形象，无法考虑患者的特定损伤限制或家庭环境。本文提出一种新型多智能体系统（MAS）架构，结合生成式AI与计算机视觉，实现远程康复闭环。框架包含四个专用微智能体：临床提取智能体从非结构化病历中提取运动约束；视频合成智能体利用基础视频生成模型创建患者专属的康复训练视频；视觉处理智能体实现动作姿态实时估计；诊断反馈智能体发出纠正指令。我们展示了系统架构，详细描述了基于大语言模型和MediaPipe的原型流程，并规划了临床评估方案。本研究证明了将生成媒体与自主决策智能体结合，可安全高效地规模化个性化患者照护。
原文摘要 · Abstract (English)
At-home physiotherapy compliance remains critically low due to a lack of personalized supervision and dynamic feedback. Existing digital health solutions rely on static, pre-recorded video libraries or generic 3D avatars that fail to account for a patient's specific injury limitations or home environment. In this paper, we propose a novel Multi-Agent System (MAS) architecture that leverages Generative AI and computer vision to close the tele-rehabilitation loop. Our framework consists of four specialized micro-agents: a Clinical Extraction Agent that parses unstructured medical notes into kinematic constraints; a Video Synthesis Agent that utilizes foundational video generation models to create personalized, patient-specific exercise videos; a Vision Processing Agent for real-time pose estimation; and a Diagnostic Feedback Agent that issues corrective instructions. We present the system architecture, detail the prototype pipeline using Large Language Models and MediaPipe, and outline our clinical evaluation plan. This work demonstrates the feasibility of combining generative media with agentic autonomous decision-making to scale personalized patient care safely and effectively.
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