Computer vision-based clinical gait analysis is the subject of permanent research. However, there are very few datasets publicly available; hence the comparison of existing methods between each other is not straightforward. Even if the test data are in an open access, existing databases contain very few test subjects and single modality measurements, which limit their usage. The contributions of this paper are three-fold. First, we propose a new open-access multi-modal database acquired with the Kinect v.2 camera for the task of gait analysis. Second, we adapt to use the skeleton joint orientation data to calculate kinematic gait parameters to match golden-standard MOCAP systems. We propose a new set of features based on 3D low-limbs flexion dynamics to analyze the symmetry of a gait. Third, we design a Long-Short Term Memory (LSTM) ensemble model to create an unsupervised gait classification tool. The results show that joint orientation data provided by Kinect can be successfully used in an inexpensive clinical gait monitoring system, with the results moderately better than reported state-of-the-art for three normal/pathological gait classes.

基于计算机视觉的临床步态分析是永久性研究的主题。但是，公开的数据集很少。因此，现有方法之间的比较不是直接的。即使测试数据处于开放访问状态，现有数据库也包含很少的测试对象和单一模态测量，这限制了它们的使用。本文的贡献是三方面的。首先，我们建议使用Kinect v.2相机获取一个新的开放式多模态数据库，用于步态分析任务。其次，我们适应使用骨骼关节方向数据来计算运动步态参数，以匹配黄金标准的MOCAP系统。我们提出了一组基于3D低肢弯曲动力学的新功能，以分析步态的对称性。第三，我们设计了一个长短时记忆（LSTM）集成模型来创建无监督步态分类工具。结果表明，由Kinect提供的关节方向数据可以成功地用于廉价的临床步态监测系统，其结果在三个正常/病理步态类别中均优于报告的最新技术水平。