患者朝向对基于AI康复监测的单视角和多视角相机环境的影响

中文摘要 AI 辅助

康复锻炼的自动化质量评估在很大程度上依赖于从视频数据中准确的人体姿态估计。尽管已经提出了许多基于RGB的姿态估计方法，但相机放置对检测临床相关运动错误的影响仍未得到充分探索。为了弥补这一空白，我们引入了REHAB26-ViewAngles数据集，该数据集包含从多种相机角度捕获的正确和错误的康复锻炼执行。此外，我们提出了一种新颖的可分离性度量，用于量化算法区分有效和错误锻炼重复的能力。利用这些工具，我们分析了在各种相机放置下，不同的基于RGB的姿态估计策略如何适用于锻炼质量评估。具体来说，我们分析了单相机2D和3D姿态估计以及四种多相机策略：两个正交2D视图的组合、3D三角测量、加权3D融合，以及一个专门从两个同步相机训练的基于AI的姿态估计Transformer模型。我们的研究结果表明，一个最优放置的2D相机可以将可分离性比常用的0°正面视图提高16.9%，并且通常优于单相机3D估计，而结合两个视图可以进一步提高准确性，最高可达13.1%。这些结果为在家庭和临床环境中部署康复监测提供了实用指导。

英文摘要

Automated quality assessment of rehabilitation exercises relies heavily on accurate human pose estimation from video data. Although numerous RGB-based pose estimation methods have been proposed, the impact of camera placement on detecting clinically relevant movement errors remains insufficiently explored. To address this gap, we introduce REHAB26-ViewAngles, a dataset comprising correct and incorrect rehabilitation exercise executions captured from a wide range of camera angles. Furthermore, we propose a novel separability metric to quantify an algorithm's ability to distinguish between valid and faulty exercise repetitions. Using these tools, we analyze how various RGB-based pose-estimation strategies are suitable for exercise quality assessment under varying camera placements. In particular, we analyze single-camera 2D and 3D pose estimation and four multi-camera strategies: a combination of two orthogonal 2D views, 3D triangulation, weighted 3D fusion, and an AI-based pose-estimation transformer model specifically trained from two synchronized cameras. Our findings reveal that an optimally placed 2D camera can improve the separability by 16.9% over the commonly used 0° frontal view and frequently outperforms single-camera 3D estimation, while combining two views can further improve accuracy by up to 13.1%. These results offer practical guidance for deploying rehabilitation monitoring in both home and clinical settings.

发表机构

• Masaryk University（马萨里克大学）
• TU Dortmund University（多特蒙德工业大学）
• VisionCraft

机构由 AI 辅助整理，请以论文原文为准。