Human Gait Symmetry Assessment using a Depth Camera and Mirrors
This work addresses gait symmetry assessment for healthcare or rehabilitation applications, but it is incremental as it builds on existing methods with a novel setup.
The paper tackled the problem of assessing human gait symmetry by introducing a system that uses a depth camera and mirrors to generate 3D point clouds, achieving reliable symmetry indices across 9 different gait types.
This paper proposes a reliable approach for human gait symmetry assessment using a depth camera and two mirrors. The input of our system is a sequence of 3D point clouds which are formed from a setup including a Time-of-Flight (ToF) depth camera and two mirrors. A cylindrical histogram is estimated for describing the posture in each point cloud. The sequence of such histograms is then separated into two sequences of sub-histograms representing two half-bodies. A cross-correlation technique is finally applied to provide values describing gait symmetry indices. The evaluation was performed on 9 different gait types to demonstrate the ability of our approach in assessing gait symmetry. A comparison between our system and related methods, that employ different input data types, is also provided.