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评估一种基于智能手机的无标记系统,用于测量膝骨关节炎患者的下肢运动学。

Evaluation of a smartphone-based markerless system to measure lower-limb kinematics in patients with knee osteoarthritis.

作者信息

Wang Junqing, Xu Wei, Wu Zhuoying, Zhang Hui, Wang Biao, Zhou Zongke, Wang Chen, Li Kang, Nie Yong

机构信息

Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University Chengdu Sichuan Province China; Department of Orthopedic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University Chengdu Sichuan Province China.

Department of Orthopedic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University Chengdu Sichuan Province China; School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China Hefei Anhui China.

出版信息

J Biomech. 2025 Mar;181:112529. doi: 10.1016/j.jbiomech.2025.112529. Epub 2025 Jan 16.

Abstract

OpenCap, a smartphone-based markerless system, offers a cost-effective alternative to traditional marker-based systems for gait analysis. However, its kinematic measurement accuracy must be evaluated before widespread use in clinical practice. This study aimed to evaluate OpenCap for lower-limb joint angle measurements during walking in patients with knee osteoarthritis (OA) and to compare error metrics between patients and healthy controls. Lower-limb kinematic data were simultaneously collected from 53 patients with knee OA and 30 healthy individuals using OpenCap and a marker-based motion capture system while walking at a self-selected speed. Evaluation was assessed through root mean square error (RMSE) and intraclass correlation coefficient (ICC). Two-way repeated measures analyses of variance were employed to evaluate the main effects of and interactions between group (knee OA patients vs. healthy controls) and walking direction (toward vs. away from the camera). The results demonstrated a grand mean RMSE of 6.1° and an ICC of 0.67 for knee OA patients when walking toward the camera. Patients with knee OA exhibited significantly higher RMSE and lower ICC values compared to healthy controls. Additionally, walking toward the camera was associated with significantly lower RMSE and higher ICC values than walking away from the camera. OpenCap's minimal hardware costs, free software, and user-friendly interface suggest its potential for widespread clinical implementation. The sagittal hip and knee angles demonstrate strong agreement with the marker-based system; however, caution is warranted in clinical decision-making for this population, as errors in most joint angles slightly surpass acceptable thresholds.

摘要

OpenCap是一种基于智能手机的无标记系统,为传统的基于标记的步态分析系统提供了一种经济高效的替代方案。然而,在临床实践中广泛应用之前,必须评估其运动学测量精度。本研究旨在评估OpenCap在膝关节骨关节炎(OA)患者行走过程中下肢关节角度测量的情况,并比较患者与健康对照之间的误差指标。在53例膝关节OA患者和30名健康个体以自选速度行走时,使用OpenCap和基于标记的运动捕捉系统同时收集下肢运动学数据。通过均方根误差(RMSE)和组内相关系数(ICC)进行评估。采用双向重复测量方差分析来评估组(膝关节OA患者与健康对照)和行走方向(朝向与远离摄像头)的主效应及相互作用。结果显示,膝关节OA患者在朝向摄像头行走时,总体平均RMSE为6.1°,ICC为0.67。与健康对照相比,膝关节OA患者的RMSE显著更高,ICC值更低。此外,与远离摄像头行走相比,朝向摄像头行走时RMSE显著更低,ICC值更高。OpenCap的硬件成本最低、软件免费且用户界面友好,表明其具有广泛临床应用的潜力。矢状面髋关节和膝关节角度与基于标记的系统显示出高度一致性;然而,对于该人群的临床决策仍需谨慎,因为大多数关节角度的误差略超过可接受阈值。

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