| Autor: |
Duc Cong Dang, Thanh Tuan Pham, Young Soo Suh |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 133557-133568 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3459964 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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