Visual Inertial Odometry-Based Gait Analysis Using Waist-Attached RGB-D Camera and Inertial Sensors

Abstract

In this article, a visual–inertial odometry algorithm is proposed to estimate walking stride length and reconstruct walking trajectory. Depth and color image data from a downward-looking waist-mounted red green blue-depth (RGB-D) camera is fused with its internal inertial measurement unit (IMU) data in an estimation algorithm to perform foot detection and position estimation. Floor plane and foot positions in stance phases are calculated and used as landmarks to construct measurement equations for updating the filter. A smoothing problem is formulated as a linear optimization problem to improve filter results. Experiments are performed to evaluate the walking trajectory reconstruction and the overall root mean square errors (RMSEs) of walking stride length estimation is about 3.8 cm.