DIRO, University of Montreal, Montreal, QC H3T 1J4, Canada.
ITF, The University of Danang-University of Science and Technology, Danang 556361, Vietnam.
Sensors (Basel). 2019 Feb 21;19(4):891. doi: 10.3390/s19040891.
In this paper, we introduce an approach for measuring human gait symmetry where the input is a sequence of depth maps of subject walking on a treadmill. Body surface normals are used to describe 3D information of the walking subject in each frame. Two different schemes for embedding the temporal factor into a symmetry index are proposed. Experiments on the whole body, as well as the lower limbs, were also considered to assess the usefulness of upper body information in this task. The potential of our method was demonstrated with a dataset of 97,200 depth maps of nine different walking gaits. An ROC analysis for abnormal gait detection gave the best result ( AUC = 0.958 ) compared with other related studies. The experimental results provided by our method confirm the contribution of upper body in gait analysis as well as the reliability of approximating average gait symmetry index without explicitly considering individual gait cycles for asymmetry detection.
在本文中，我们介绍了一种用于测量人体步态对称性的方法，该方法的输入是受试者在跑步机上行走的一系列深度图。在每一帧中，都使用体表面法向量来描述行走受试者的 3D 信息。我们提出了两种将时间因素嵌入对称指数的不同方案。还考虑了对整个身体以及下肢的实验，以评估在该任务中使用上半身信息的有用性。我们的方法在包含 9 种不同步态的 97200 个深度图的数据集上进行了验证。与其他相关研究相比，异常步态检测的 ROC 分析给出了最佳结果（AUC=0.958）。我们的方法提供的实验结果证实了上半身在步态分析中的贡献，以及在不明确考虑个体步态周期进行不对称检测的情况下，近似平均步态对称指数的可靠性。
