Agentic AI for Personalized Physiotherapy: A Multi-Agent Framework for Generative Video Training and Real-Time Pose Correction

TL;DR

This paper introduces a multi-agent AI system that personalizes physiotherapy training at home by generating tailored videos and providing real-time pose correction, aiming to improve patient compliance and outcomes.

Contribution

It presents a novel multi-agent framework integrating generative AI and computer vision for personalized, dynamic tele-rehabilitation support.

Findings

System architecture and prototype pipeline demonstrated feasibility.

Combines generative media with autonomous decision-making.

Outlines clinical evaluation plan for effectiveness.

Abstract

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.