Precise Foot Feature Point Localization and Automatic Parameters Measurement

In order to quickly obtain foot parameters and quantify the degree of foot deformation, an algorithm that can accurately locate foot feature points and automatically calculate foot parameters is proposed. First, a total of 93 patients participate and their foot models are obtained using the UPOD las...

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Main Author: JI Mian, LIN Yanping, WANG Dongmei, CHEN Li, MA Xin
Format: Article
Language:zho
Published: Editorial Office of Journal of Shanghai Jiao Tong University 2025-05-01
Series:Shanghai Jiaotong Daxue xuebao
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Online Access:https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-5-703.shtml
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author JI Mian, LIN Yanping, WANG Dongmei, CHEN Li, MA Xin
author_facet JI Mian, LIN Yanping, WANG Dongmei, CHEN Li, MA Xin
author_sort JI Mian, LIN Yanping, WANG Dongmei, CHEN Li, MA Xin
collection DOAJ
description In order to quickly obtain foot parameters and quantify the degree of foot deformation, an algorithm that can accurately locate foot feature points and automatically calculate foot parameters is proposed. First, a total of 93 patients participate and their foot models are obtained using the UPOD laser scanner. Then, the random sampling consensus algorithm and principal component analysis are used to align the foot coordinate system. The algorithm utilizes foot features to identify and locate feature points, enabling the parameter calculation of length, angle, and girth. The accuracy, repeatability, and consistency of the measurements are evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), interclass correlation coefficient (ICC), and Bland-Altman plots. The MAE of foot length and width is less than 2 mm, and for ball girth, instep girth, and heel girth, it is less than 4 mm. The MAPE is less than 2%, and the ICCs for the three replicates exceed 0.99. More than 95% of the scattered points in the Bland-Altman plots are within the consistency boundary. The results show that the proposed algorithm can automatically align the coordinate system, accurately locate feature points, and accurately measure foot parameters in the standing posture. The measurement accuracy meets clinical needs with high accuracy and reliability. The findings provide valuable data support for foot classification, intelligent assistive device adaptation, and personalized assistive device design, showing important clinical application potential.
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issn 1006-2467
language zho
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publisher Editorial Office of Journal of Shanghai Jiao Tong University
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spelling doaj-art-6bda7d18221d449bbbefe9621307afe22025-08-20T03:24:46ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672025-05-0159570371010.16183/j.cnki.jsjtu.2023.397Precise Foot Feature Point Localization and Automatic Parameters MeasurementJI Mian, LIN Yanping, WANG Dongmei, CHEN Li, MA Xin01. Institute of Biomedical Manufacturing and Quality of Life Engineering, Shanghai Jiao Tong University, Shanghai 200240, China2. Department of Foot and Ankle Surgery, Huashan Hospital, Fudan University, Shanghai 200040, ChinaIn order to quickly obtain foot parameters and quantify the degree of foot deformation, an algorithm that can accurately locate foot feature points and automatically calculate foot parameters is proposed. First, a total of 93 patients participate and their foot models are obtained using the UPOD laser scanner. Then, the random sampling consensus algorithm and principal component analysis are used to align the foot coordinate system. The algorithm utilizes foot features to identify and locate feature points, enabling the parameter calculation of length, angle, and girth. The accuracy, repeatability, and consistency of the measurements are evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), interclass correlation coefficient (ICC), and Bland-Altman plots. The MAE of foot length and width is less than 2 mm, and for ball girth, instep girth, and heel girth, it is less than 4 mm. The MAPE is less than 2%, and the ICCs for the three replicates exceed 0.99. More than 95% of the scattered points in the Bland-Altman plots are within the consistency boundary. The results show that the proposed algorithm can automatically align the coordinate system, accurately locate feature points, and accurately measure foot parameters in the standing posture. The measurement accuracy meets clinical needs with high accuracy and reliability. The findings provide valuable data support for foot classification, intelligent assistive device adaptation, and personalized assistive device design, showing important clinical application potential.https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-5-703.shtmlfoot measurementcoordinate alignmentfeature point localization3d point clouds
spellingShingle JI Mian, LIN Yanping, WANG Dongmei, CHEN Li, MA Xin
Precise Foot Feature Point Localization and Automatic Parameters Measurement
Shanghai Jiaotong Daxue xuebao
foot measurement
coordinate alignment
feature point localization
3d point clouds
title Precise Foot Feature Point Localization and Automatic Parameters Measurement
title_full Precise Foot Feature Point Localization and Automatic Parameters Measurement
title_fullStr Precise Foot Feature Point Localization and Automatic Parameters Measurement
title_full_unstemmed Precise Foot Feature Point Localization and Automatic Parameters Measurement
title_short Precise Foot Feature Point Localization and Automatic Parameters Measurement
title_sort precise foot feature point localization and automatic parameters measurement
topic foot measurement
coordinate alignment
feature point localization
3d point clouds
url https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-5-703.shtml
work_keys_str_mv AT jimianlinyanpingwangdongmeichenlimaxin precisefootfeaturepointlocalizationandautomaticparametersmeasurement