Estimation of gait normality index based on point clouds through deep auto-encoder

• Published in: EURASIP journal on image and video processing Vol. 2019; no. 1; pp. 1 - 13
• Main Authors: Nguyen, Trong-Nguyen, Meunier, Jean
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 28.05.2019; Springer Nature B.V; SpringerOpen
• Subjects: Artificial neural networks; Auto-encoder; Biometrics; Cylinders; Deep network; Depth indicators; Engineering; Gait; Histograms; Image Processing and Computer Vision; Machine learning; Normality; Normality index; Pattern Recognition; Signal,Image and Speech Processing; Three dimensional models; Walking; Deep network; Gait; Auto-encoder; Normality index
• ISSN: 1687-5281; 1687-5176; 1687-5281
• DOI: 10.1186/s13640-019-0466-z

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.