Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views, and has the potential to make movement analysis easy and quick, including gait analysis. This could enable much more frequent and quantitative characterization of gait impairments, allowing better monitoring of outcomes and responses to interventions. However, the impact of different keypoint detectors and reconstruction algorithms on markerless pose estimation accuracy has not been thoroughly evaluated. We tested these algorithmic choices on data acquired from a multicamera system from a heterogeneous sample of 25 individuals seen in a rehabilitation hospital. We found that using a top-down keypoint detector and reconstructing trajectories with an implicit function enabled accurate, smooth and anatomically plausible trajectories, with a noise in the step width estimates compared to a GaitRite walkway of only 8mm.

翻译：无标记姿态估计能够从多个同步且校准的视角重建人体运动，有望使包括步态分析在内的运动分析变得简便快捷。这将有助于更频繁、定量地描述步态障碍特征，从而更好地监测干预结果和治疗反应。然而，不同关键点检测器和重建算法对无标记姿态估计精度的影响尚未得到充分评估。我们在康复医院采集的25例异质性样本的多摄像头系统数据上测试了这些算法选择。研究发现，采用自顶向下的关键点检测器并结合隐函数进行轨迹重建，能够获得精确、平滑且符合解剖学特征的轨迹，与GaitRite步道相比，步幅宽度估计的噪声仅为8毫米。