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

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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.
Funding Information
  • 2023 Research Fund of University of Ulsan

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