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

• URL: http://arxiv.org/abs/2604.21154v1
• Date: Wed, 22 Apr 2026 23:47:51 GMT
• Title: Agentic AI for Personalized Physiotherapy: A Multi-Agent Framework for Generative Video Training and Real-Time Pose Correction
• Abstract summary: We propose a novel Multi-Agent System (MAS) architecture that leverages Generative AI and computer vision to close the tele-rehabilitation loop.<n>We present the system architecture, detail the prototype pipeline using Large Language Models and MediaPipe, and outline our clinical evaluation plan.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• ComBodied Agents: a New Paradigm of Human-Centric Agentic AI [121.60938707072069]
  We introduce Combodied Agents, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time.<n>We unify fragmented capabilities across personal assistants, health agents, AI companions, and adaptive human--AI systems into a closed loop.
  arXiv  Detail & Related papers  (2026-08-11T13:35:58Z)
• Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration [53.772014300855375]
  We introduce NIAgent, a multi-agent system for autonomous end-to-end neuroimaging analysis.<n>Unlike conventional flat tool-calling agents, NIAgent adopts a code-centric execution paradigm.<n>We propose a hierarchical verification framework for autonomous quality control, integrating cohort-level metric screening with agentic visual inspection.
  arXiv  Detail & Related papers  (2026-05-10T06:30:19Z)
• Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis [19.715805184063168]
  Agentic AI offers a new solution, eliminating the need for intrinsic 3D processing.<n>We present a training-free agentic pipeline for automated brain MRI analysis.<n>Our results demonstrate that agentic AI can solve highly neuro-radiological image analysis tasks through tool use without the need for training or fine-tuning.
  arXiv  Detail & Related papers  (2026-04-17T22:04:16Z)
• MedOpenClaw: Auditable Medical Imaging Agents Reasoning over Uncurated Full Studies [43.17392932337315]
  Currently, evaluating vision-language models (VLMs) in medical imaging tasks oversimplifies clinical reality by relying on pre-selected 2D images.<n>We propose MEDOPENCLAW, an auditable runtime designed to let VLMs operate dynamically within standard medical tools or viewers.<n>We also introduce MEDFLOWBENCH, a full-study medical imaging benchmark covering brain MRI and lung CT/PET.
  arXiv  Detail & Related papers  (2026-03-25T17:33:58Z)
• EvoClinician: A Self-Evolving Agent for Multi-Turn Medical Diagnosis via Test-Time Evolutionary Learning [72.70291772077738]
  We propose Med-Inquire, a new benchmark designed to evaluate an agent's ability to perform multi-turn diagnosis.<n>We then introduce EvoClinician, a self-evolving agent that learns efficient diagnostic strategies at test time.<n>Our experiments show EvoClinician outperforms continual learning baselines and other self-evolving agents like memory agents.
  arXiv  Detail & Related papers  (2026-01-30T13:26:18Z)
• Lightweight Structured Multimodal Reasoning for Clinical Scene Understanding in Robotics [4.253383204461325]
  We present a lightweight agentic multimodal framework for video-based scene understanding.<n>It supports chain-of-thought reasoning, speech-vision fusion, and dynamic tool invocation.
  arXiv  Detail & Related papers  (2025-09-26T07:49:49Z)
• MedVSR: Medical Video Super-Resolution with Cross State-Space Propagation [63.38824041721275]
  Low-resolution (LR) medical videos present unique challenges for video super-resolution (VSR) models.<n>We propose MedVSR, a tailored framework for medical VSR.<n>We show that MedVSR significantly outperforms existing VSR models in reconstruction performance and efficiency.
  arXiv  Detail & Related papers  (2025-09-25T14:56:59Z)
• Does DINOv3 Set a New Medical Vision Standard? [67.33543059306938]
  This report investigates whether DINOv3 can serve as a powerful unified encoder for medical vision tasks without domain-specific pre-training.<n>We benchmark DINOv3 across common medical vision tasks, including 2D/3D classification and segmentation.<n>Remarkably, it can even outperform medical-specific foundation models like BiomedCLIP and CT-Net on several tasks.
  arXiv  Detail & Related papers  (2025-09-08T09:28:57Z)
• Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection [12.245379864678291]
  We tackle unsupervised medical anomaly detection proposing a modernized autoencoder-based framework, the Q-Former Autoencoder.<n>We directly utilize frozen vision foundation models as feature extractors, enabling rich, multi-stage, high-level representations without domain-specific fine-tuning.<n>Our results highlight the potential of vision foundation model encoders, pretrained on natural images, to generalize effectively to medical image analysis tasks without further fine-tuning.
  arXiv  Detail & Related papers  (2025-07-24T14:55:33Z)
• Learning to Be A Doctor: Searching for Effective Medical Agent Architectures [32.82784216021035]
  This paper introduces a novel framework for the automated design of medical agent architectures.<n>Motivated by the success of automated machine learning (AutoML), we define a hierarchical and expressive agent search space.<n>Our framework conceptualizes medical agents as graph-based architectures composed of diverse, functional node types.
  arXiv  Detail & Related papers  (2025-04-15T15:44:21Z)
• Endora: Video Generation Models as Endoscopy Simulators [53.72175969751398]
  This paper introduces model, an innovative approach to generate medical videos that simulate clinical endoscopy scenes. We also pioneer the first public benchmark for endoscopy simulation with video generation models. Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research.
  arXiv  Detail & Related papers  (2024-03-17T00:51:59Z)
• Safe AI for health and beyond -- Monitoring to transform a health service [51.8524501805308]
  We will assess the infrastructure required to monitor the outputs of a machine learning algorithm. We will present two scenarios with examples of monitoring and updates of models.
  arXiv  Detail & Related papers  (2023-03-02T17:27:45Z)
• Multimodal Semantic Scene Graphs for Holistic Modeling of Surgical Procedures [70.69948035469467]
  We take advantage of the latest computer vision methodologies for generating 3D graphs from camera views. We then introduce the Multimodal Semantic Graph Scene (MSSG) which aims at providing unified symbolic and semantic representation of surgical procedures.
  arXiv  Detail & Related papers  (2021-06-09T14:35:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.