Gait Analysis Using Single Waist-Mounted RGB-D Camera and Dual Foot-Mounted IMUs

Abstract

Accurate estimation of dual walking trajectories remains a challenge in human gait tracking systems due to limitations in sensor precision and data integration methods. To address these issues, this paper presents a novel human gait tracking system that integrates a downward-looking waist-mounted red-green-blue-depth (RGB-D) camera with two inertial measurement units (IMUs) mounted on each foot. Our approach utilizes a fully convolutional network (FCN) for precise foot detection from RGB-D images. The positions of both feet are then computed using the detected foot and the camera’s rotation matrix relative to the floor plane. These position estimates are incorporated into a Kalman filter, with a quadratic optimization-based smoothing method applied to improve accuracy. Experimental results demonstrate a significant improvement in dual trajectory estimation, achieving a root mean square error (RMSE) of 3.3 cm in stride length estimation. This system enhances the accuracy and reliability of gait analysis, effectively addressing the limitations of existing methods.

Keywords

• Deep learning
• gait analysis
• IMUs
• Kalman filter
• RGB-D camera