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使用惯性传感器估算自行车骑行过程中的 3D 膝关节角度:基于蹬踏运动的新型传感器到肢体标定程序的准确性。

Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion.

机构信息

M2S Laboratory (Movement, Sports & Health), University Rennes 2, ENS Rennes, 35170 Bruz, France.

MIMETIC-Analysis-Synthesis Approach for Virtual Human Simulation, INRIA Rennes-Bretagne Atlantique, IRISA_D6-MEDIA ET INTERACTIONS, 35000 Rennes, France.

出版信息

Sensors (Basel). 2019 May 30;19(11):2474. doi: 10.3390/s19112474.

DOI:10.3390/s19112474
PMID:31151200
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6603641/
Abstract

This paper presents a novel sensor-to-segment calibration procedure for inertial sensor-based knee joint kinematics analysis during cycling. This procedure was designed to be feasible in-field, autonomously, and without any external operator or device. It combines a static standing up posture and a pedaling task. The main goal of this study was to assess the accuracy of the new sensor-to-segment calibration method (denoted as the 'cycling' method) by calculating errors in terms of body-segment orientations and 3D knee joint angles using inertial measurement unit (IMU)-based and optoelectronic-based motion capture. To do so, 14 participants were evaluated during pedaling motion at a workload of 100 W, which enabled comparisons of the cycling method with conventional calibration methods commonly employed in gait analysis. The accuracy of the cycling method was comparable to that of other methods concerning the knee flexion/extension angle, and did not exceed 3.8°. However, the cycling method presented the smallest errors for knee internal/external rotation (6.65 ± 1.94°) and abduction/adduction (5.92 ± 2.85°). This study demonstrated that a calibration method based on the completion of a pedaling task combined with a standing posture significantly improved the accuracy of 3D knee joint angle measurement when applied to cycling analysis.

摘要

本文提出了一种新的基于惯性传感器的膝关节运动学分析的传感器到肢体标定方法,用于骑行过程中。该方法旨在现场、自主且无需任何外部操作员或设备的情况下实现。它结合了静态站立姿势和踩踏任务。本研究的主要目的是通过使用基于惯性测量单元(IMU)和光电运动捕捉的方法,计算身体节段方向和 3D 膝关节角度的误差,来评估新的传感器到肢体标定方法(称为“骑行”方法)的准确性。为此,在 100W 工作负荷下的踩踏运动期间评估了 14 名参与者,使骑行方法与通常用于步态分析的传统标定方法进行了比较。骑行方法在膝关节屈伸角度方面与其他方法的准确性相当,误差不超过 3.8°。然而,骑行方法在膝关节内/外旋(6.65±1.94°)和外展/内收(5.92±2.85°)方面呈现出最小的误差。本研究表明,基于踩踏任务完成和站立姿势的标定方法显著提高了 3D 膝关节角度测量在骑行分析中的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/f8d2e947d1d9/sensors-19-02474-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/260259f28c1f/sensors-19-02474-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/390a16c68358/sensors-19-02474-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/9bba713c8604/sensors-19-02474-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/49efd8bbe5e3/sensors-19-02474-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/f8d2e947d1d9/sensors-19-02474-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/260259f28c1f/sensors-19-02474-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/390a16c68358/sensors-19-02474-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/9bba713c8604/sensors-19-02474-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/49efd8bbe5e3/sensors-19-02474-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/6603641/f8d2e947d1d9/sensors-19-02474-g005.jpg

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