This paper proposes a method estimating an index that indicates human gait normality based on a sequence of 3D point clouds representing the walking motion of a subject. A cylinder-based histogram is extracted from each cloud to reduce the number of data dimensions as well as highlight gait-related characteristics. A model of deep neural network is finally formed from such histograms of normal gait patterns to provide gait normality indices supporting gait assessment tasks. The ability of our approach is demonstrated using a dataset of 9 different gait types performed by 9 subjects and two other datasets converted from mocap data. The experimental results are also compared with other related methods that process different input data types including silhouette, depth map, and skeleton as well as state-of-the-art deep learning approaches working on point cloud.

中文翻译：

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深度自动编码器基于点云的步态正常指数估算

本文提出了一种基于表示对象步行运动的3D点云序列来估计表示人的步态正常性的指标的方法。从每个云中提取基于圆柱的直方图，以减少数据维度的数量以及突出步态相关的特征。最终从正常步态模式的直方图形成一个深度神经网络模型，以提供支持步态评估任务的步态正态性指标。我们的方法的能力通过使用由9位受试者执行的9种不同步态类型的数据集以及从mocap数据转换而来的另外两个数据集得到证明。还将实验结果与处理不同输入数据类型（包括轮廓，深度图，