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:无标记的外观估计能够从多重同步和校准的观点中重建人类运动,并有可能使移动分析容易和快速,包括步态分析。这可以更频繁和量化地描述动作障碍,从而能够更好地监测结果和干预反应。然而,不同关键点探测器和重建算法对无标记者的影响没有进行彻底的评估,使没有标记者具有估计准确性。我们测试了从多镜头系统获得的数据中的这些算法选择,这些数据来自康复医院的25个样本。我们发现,使用自上而下的关键点探测器,并重建带有隐含功能的轨迹,能够准确、顺畅和从解剖学上看似合理的轨迹,与仅8毫米的盖特利特人行道相比,步宽估计有噪音。</s>