Abromavičius Vytautas, Gisleris Ervinas, Daunoravičienė Kristina, Žižienė Jurgita, Serackis Artūras, Maskeliūnas Rytis
Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania.
Department of Electronic Systems, Vilnius Gediminas Technical University, Vilnius, Lithuania.
PLoS One. 2025 Aug 7;20(8):e0328969. doi: 10.1371/journal.pone.0328969. eCollection 2025.
Accurate and reliable skeletal motion tracking is essential for rehabilitation monitoring, enabling objective assessment of patient progress and facilitating telerehabilitation applications. Traditional marker-based motion capture systems, while highly accurate, are costly and impractical for home rehabilitation, whereas marker-less methods often suffer from depth estimation errors and occlusions. Recent studies have explored various computer vision and deep learning approaches for human pose estimation, yet challenges remain in ensuring robust depth accuracy and tracking under occlusion conditions. This study proposes a three-dimensional human skeleton tracking system for upper limb activities that integrates temporal and spatial synchronization to improve depth estimation accuracy for rehabilitation exercises. The proposed system combines a 90° secondary camera to compensate for the depth prediction inaccuracies inherent in single-camera systems, reducing error margins by up to 0.4 m. In addition, a linear regression-based depth error correction model is implemented to refine depth coordinates, further improving tracking precision. The Kalman filtering framework is employed to enhance temporal consistency, allowing real-time interpolation of missing joint positions. Experimental results demonstrate that the proposed method significantly reduces depth estimation errors of the elbow and wrist joint (p < 0.001) compared to single camera setups, particularly in scenarios involving occlusions and non-frontal perspectives. This study provides a cost-effective and scalable solution for remote patient monitoring and motor function evaluation.
准确可靠的骨骼运动跟踪对于康复监测至关重要，能够对患者的进展进行客观评估，并促进远程康复应用。传统的基于标记的运动捕捉系统虽然精度很高，但成本高昂，对于家庭康复来说不切实际，而无标记方法往往存在深度估计误差和遮挡问题。最近的研究探索了各种用于人体姿态估计的计算机视觉和深度学习方法，但在确保遮挡条件下的稳健深度精度和跟踪方面仍然存在挑战。本研究提出了一种用于上肢活动的三维人体骨骼跟踪系统，该系统集成了时间和空间同步，以提高康复训练的深度估计精度。所提出的系统结合了一个90°辅助摄像头，以补偿单摄像头系统固有的深度预测不准确问题，将误差幅度降低了多达0.4米。此外，实施了基于线性回归的深度误差校正模型来细化深度坐标，进一步提高跟踪精度。采用卡尔曼滤波框架来增强时间一致性，允许对缺失的关节位置进行实时插值。实验结果表明，与单摄像头设置相比，所提出的方法显著降低了肘部和腕关节的深度估计误差（p < 0.001），特别是在涉及遮挡和非正面视角的场景中。本研究为远程患者监测和运动功能评估提供了一种经济高效且可扩展的解决方案。
