Real-time foot tracking and gait evaluation with geometric modeling
Gait evaluation is important in gait rehabilitation and assistance to monitor patient's balance status and assess recovery performance. Recent technologies leverage on vision-based systems with high portability and low operational complexity. In this paper, we propose a new vision-based foot tr...
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sg-ntu-dr.10356-1601442022-07-13T07:27:32Z Real-time foot tracking and gait evaluation with geometric modeling Foo, Ming Jeat Chang, Jen-Shuan Ang, Wei Tech School of Mechanical and Aerospace Engineering Rehabilitation Research Institute of Singapore (RRIS) Engineering::Mechanical engineering Gait Evaluation Human Motion Gait evaluation is important in gait rehabilitation and assistance to monitor patient's balance status and assess recovery performance. Recent technologies leverage on vision-based systems with high portability and low operational complexity. In this paper, we propose a new vision-based foot tracking algorithm specially catering to overground gait assistive devices, which often have limited view of the users. The algorithm models the foot and the shank of the user using simple geometry. Through cost optimization, it then aligns the models to the point cloud, showing the back view of the user's lower limbs. The system outputs the poses of the feet, which are used to compute the spatial-temporal gait parameters. Seven healthy young subjects are recruited to perform overground and treadmill walking trials. The results of the algorithm are compared with the motion capture system and a third-party gait analysis software. The algorithm has a fitting rotational and translational errors of less than 20 degrees and 33 mm, respectively, for 0.4 m/s walking speed. The gait detection F1 score achieves more than 96.8%. The step length and step width errors are around 35 mm, while the cycle time error is less than 38 ms. The proposed algorithm provides a fast, contactless, portable, and cost-effective gait evaluation method without requiring the user to wear any customized footwear. Published version This work is supported by SG Health Assistive and Robotics Programme (SHARP) Grant—Development and POC of Care Assistant and Rehabilitation Enabling (CARE) Robots (SERC 1922200003). 2022-07-13T07:27:31Z 2022-07-13T07:27:31Z 2022 Journal Article Foo, M. J., Chang, J. & Ang, W. T. (2022). Real-time foot tracking and gait evaluation with geometric modeling. Sensors, 22(4), 1661-. https://dx.doi.org/10.3390/s22041661 1424-8220 https://hdl.handle.net/10356/160144 10.3390/s22041661 35214563 2-s2.0-85124904314 4 22 1661 en SERC 1922200003 Sensors © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
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Engineering::Mechanical engineering Gait Evaluation Human Motion Foo, Ming Jeat Chang, Jen-Shuan Ang, Wei Tech Real-time foot tracking and gait evaluation with geometric modeling |
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Gait evaluation is important in gait rehabilitation and assistance to monitor patient's balance status and assess recovery performance. Recent technologies leverage on vision-based systems with high portability and low operational complexity. In this paper, we propose a new vision-based foot tracking algorithm specially catering to overground gait assistive devices, which often have limited view of the users. The algorithm models the foot and the shank of the user using simple geometry. Through cost optimization, it then aligns the models to the point cloud, showing the back view of the user's lower limbs. The system outputs the poses of the feet, which are used to compute the spatial-temporal gait parameters. Seven healthy young subjects are recruited to perform overground and treadmill walking trials. The results of the algorithm are compared with the motion capture system and a third-party gait analysis software. The algorithm has a fitting rotational and translational errors of less than 20 degrees and 33 mm, respectively, for 0.4 m/s walking speed. The gait detection F1 score achieves more than 96.8%. The step length and step width errors are around 35 mm, while the cycle time error is less than 38 ms. The proposed algorithm provides a fast, contactless, portable, and cost-effective gait evaluation method without requiring the user to wear any customized footwear. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Foo, Ming Jeat Chang, Jen-Shuan Ang, Wei Tech |
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Article |
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Foo, Ming Jeat Chang, Jen-Shuan Ang, Wei Tech |
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Foo, Ming Jeat |
title |
Real-time foot tracking and gait evaluation with geometric modeling |
title_short |
Real-time foot tracking and gait evaluation with geometric modeling |
title_full |
Real-time foot tracking and gait evaluation with geometric modeling |
title_fullStr |
Real-time foot tracking and gait evaluation with geometric modeling |
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Real-time foot tracking and gait evaluation with geometric modeling |
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real-time foot tracking and gait evaluation with geometric modeling |
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2022 |
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https://hdl.handle.net/10356/160144 |
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