Agentic Pipeline for Self-Synchronized Multiview Joint Angle Monitoring in Uncalibrated Environments

2026-05-19 · Source: cs.CV updates on arXiv.org · Field: Health & Wellbeing — Health & Medical Research, Medical Devices & Health Technology, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A novel agentic pipeline has been developed for self-synchronized multi-view joint angle monitoring in uncalibrated environments, specifically targeting long-term rehabilitation for spinal cord injury (SCI) patients. This system utilizes two consumer-grade RGB cameras, such as smartphones, without requiring hardware synchronization or pre-calibrated intrinsic/extrinsic parameters. Multimodal large language models (MLLMs) act as autonomous agents to manage video synchronization, target subject identification, and quality control. The pipeline employs state-of-the-art monocular 2D pose estimation models, like Sapiens with 2.0B parameters, to extract candidate poses, which are then refined through an agent-based selection mechanism. These 2D poses are optimized using explicit geometric modeling to estimate joint angles. Validated against a Vicon system, the method achieved a Mean Absolute Error (MAE) of 5.97° ± 2.36° and a Pearson correlation coefficient of 0.962 ± 0.014, demonstrating strong performance in both healthy subjects and SCI patients.

Key takeaway

For Computer Vision Engineers developing rehabilitation monitoring systems, this agentic pipeline offers a practical solution for home-based kinematic analysis. You can achieve accurate joint angle tracking using readily available consumer cameras without complex calibration or hardware synchronization, significantly reducing deployment burden. Consider integrating MLLMs for autonomous synchronization and quality control to enhance system robustness and patient self-deployment capabilities.

Key insights

An MLLM-driven pipeline enables accurate, self-synchronized multi-view joint angle monitoring using uncalibrated consumer cameras.

Principles

• Agentic MLLMs can automate complex vision tasks.
• Geometric modeling enhances interpretability of 3D pose.
• Adaptive sampling reduces MLLM query costs.

Method

The method involves local facial anonymization, MLLM-driven multi-view video synchronization, 2D pose estimation (Sapiens), agent-based target selection, and geometry-based 2D-to-3D lifting with bundle adjustment for joint angle calculation.

In practice

• Use MLLMs for video synchronization without hardware triggers.
• Apply Kalman filters for robust identity tracking in 2D poses.
• Employ RANSAC for fundamental matrix estimation in uncalibrated setups.

Topics

• Agentic Pipeline
• Multimodal Large Language Models
• Markerless Motion Capture
• Spinal Cord Injury Rehabilitation
• Joint Angle Monitoring

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.