发表机构
Singapore University of Technology and Design（新加坡科技设计大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出用一阶时间插值恢复单目步态分析中缺失的踝关节关键点，将膝关节角度误差从23.4°降至1.1°，证明简单方法可有效替代复杂学习模型。
AI 中文摘要
单目姿态估计能够实现低成本步态分析，但容易受到由遮挡、检测错误或为提高效率而减少模型规模所导致的缺失关键点的影响。尽管先前关于恢复缺失关节的研究侧重于复杂的学习模型，但简单时间方法的有效性仍未得到充分探索。我们在缺失踝关节关键点的条件下评估膝关节角度估计，并测试一阶时间插值方案作为恢复机制。在527帧单目行走视频（其中428帧具有有效的基线检测）中，移除踝关节关键点使平均角度误差增加到23.4°±46.7°，并将信号方差降至接近零。时间插值将误差降低至1.1°±6.7°，并将方差和平滑度恢复到基线的百分之几以内。这些结果表明，步态信号具有足够的时间冗余性，使得一种简单、计算量极小的插值方案能够恢复关键缺失关节，而无需借助学习重建模型。该发现支持在资源受限或易受遮挡的单目环境中，采用低复杂度、实时兼容的步态分析设计。
英文摘要
Monocular pose estimation enables low-cost gait analysis but is sensitive to missing keypoints caused by occlusion, detection errors, or efficiency-driven model reduction. While prior work on recovering missing joints focuses on complex learned models, the effectiveness of simple temporal methods remains underexplored. We evaluate knee-angle estimation under a missing-ankle-keypoint condition and test a first-order temporal interpolation scheme as a recovery mechanism. Across 527 frames of monocular walking video (428 with valid baseline detections), removing the ankle keypoint increased mean angular error to 23.4° +/- 46.7° and collapsed signal variance to near zero. Temporal interpolation reduced error to 1.1° +/- 6.7° and restored variance and smoothness to within a few percent of baseline. These results indicate that gait signals possess sufficient temporal redundancy for a simple, computationally trivial interpolation scheme to recover a critical missing joint, without resorting to learned reconstruction models. The findings support low-complexity, real-time-compatible designs for gait analysis in resource-constrained or occlusion-prone monocular settings.
Comments5 pages, 1 figure
