Analysis of 3D human gait reconstructed with a depth camera and mirrors
The problem of assessing human gaits has received a great attention in the literature since gait analysis is one of key components in healthcare. Marker-based and multi-camera systems are widely employed to deal with this problem. However, such systems usually require specific equipments with high price and/or high computational cost. In order to reduce the cost of devices, we focus on a system of gait analysis which employs only one depth sensor. The principle of our work is similar to multi-camera systems, but the collection of cameras is replaced by one depth sensor and mirrors. Each mirror in our setup plays the role of a camera which captures the scene at a different viewpoint. Since we use only one camera, the step of synchronization can thus be avoided and the cost of devices is also reduced.  Our studies can be separated into two categories: 3D reconstruction and gait analysis. The result of the former category is used as the input of the latter one. Our system for 3D reconstruction is built with a depth camera and two mirrors. Two types of depth sensor, which are distinguished based on the scheme of depth estimation, have been employed in our works. With the structured light (SL) technique integrated into the Kinect 1, we perform the 3D reconstruction based on geometrical optics. In order to increase the level of details of the 3D reconstructed model, the Kinect 2 with time-of-flight (ToF) depth measurement is used for image acquisition instead of the previous generation. However, due to multiple reflections on the mirrors, depth distortion occurs in our setup. We thus propose a simple approach for reducing such distortion before applying geometrical optics to reconstruct a point cloud of the 3D object.  For the task of gait analysis, we propose various alternative approaches focusing on the problem of gait normality/symmetry measurement. They are expected to be useful for clinical treatments such as monitoring patient's recovery after surgery. These methods consist of model-free and model-based approaches that have different cons and pros. In this dissertation, we present 3 methods that directly process point clouds reconstructed from the previous work. The first one uses cross-correlation of left and right half-bodies to assess gait symmetry while the other ones employ deep auto-encoders to measure gait normality.
Geometrical Optics Depth Distortion Space Carving Point Cloud Mirror Kinect Gait Normality Gait Symmetry Adversarial Auto-Encoder Cylindrical Histogram Cross-Correlation Adverse