• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

比较基于 3D 运动捕捉的关节角度的计算姿势估计模型。

Comparison of computational pose estimation models for joint angles with 3D motion capture.

机构信息

Centre for Trials Research, School of Medicine, Cardiff University, CF14 4YU, UK.

Musculoskeletal Biomechanics Research Facility, School of Engineering, Cardiff University, CF24 3AA, UK.

出版信息

J Bodyw Mov Ther. 2024 Oct;40:315-319. doi: 10.1016/j.jbmt.2024.04.033. Epub 2024 Apr 16.

DOI:10.1016/j.jbmt.2024.04.033
PMID:39593603
Abstract

Tools to calculate human movement patterns can benefit musculoskeletal clinicians and researchers for rehabilitation assessments. The research objective of this study was to compare two human pose estimation models (HRNet, MediaPipe) against the laboratory marker-based reference standard for joint angles and range of motion (ROM) for several movement parameters. Twenty-two healthy volunteers (Female n = 16, Male n = 6), participated to compare outputs for knee and elbow kinematics. Joint angles were calculated by selecting three marker points defining the joint and angle between them in Qualisys Track Manager software. Using predicted key points, pose estimation model calculations for the same musculoskeletal kinematic outputs were computed. Coefficient of Variation (CoV) was used as a variation statistic for joint angle during movements. All comparison results were under 10%, implying that both models compute reliable joint angle data during the five tested activities. When comparing ROM as a discrete parameter, CoV values remain low, though not all below 10%. Intra-class Correlation Coefficients were computed across the ROM data as a measure of statistical similarity. Each exercise displayed good-excellent and significant correlations for both models compared to Qualisys apart from left knee sit-to-stand. Exploration from this data sampling imply that flexion/extension exercises give stronger consistency results than full sit-to-stand movements when compared to 3D motion analysis, and there is little distinction between these two models. Finer tuning of models will give further reliability for in-depth analysis as these results are restricted, but valuable for a rehabilitative setting with limited objective analysis alternative.

摘要

计算人体运动模式的工具可以使肌肉骨骼临床医生和研究人员受益于康复评估。本研究的目的是比较两种人体姿态估计模型(HRNet、MediaPipe)与实验室基于标记的关节角度和运动范围(ROM)参考标准,以评估几个运动参数。22 名健康志愿者(女性 n=16,男性 n=6)参与了比较膝关节和肘关节运动学的研究。关节角度通过在 Qualisys Track Manager 软件中选择三个定义关节的标记点和它们之间的夹角来计算。使用预测的关键点,计算相同肌肉骨骼运动学输出的姿势估计模型计算。变异系数(CoV)用作运动过程中关节角度的变化统计量。所有比较结果均低于 10%,这意味着两种模型都可以在五种测试活动中计算出可靠的关节角度数据。当将 ROM 作为离散参数进行比较时,CoV 值仍然较低,尽管并非所有值都低于 10%。还计算了 ROM 数据的组内相关系数,作为统计相似性的度量。除了左膝从坐到站,与 Qualisys 相比,这两种模型在所有练习中都显示出良好到优秀的显著相关性。从这些数据采样中可以看出,与 3D 运动分析相比,屈伸运动比全坐立运动更能提供一致的结果,而这两种模型之间几乎没有区别。对模型进行更精细的调整将为深入分析提供更高的可靠性,因为这些结果是有限的,但对于康复环境中缺乏客观分析替代方法来说是有价值的。

相似文献

1
Comparison of computational pose estimation models for joint angles with 3D motion capture.比较基于 3D 运动捕捉的关节角度的计算姿势估计模型。
J Bodyw Mov Ther. 2024 Oct;40:315-319. doi: 10.1016/j.jbmt.2024.04.033. Epub 2024 Apr 16.
2
Role of joint interactions in upper limb joint movements: a disability simulation study using wearable inertial sensors for 3D motion capture.关节相互作用在上肢关节运动中的作用:使用可穿戴惯性传感器进行 3D 运动捕捉的残疾模拟研究。
J Neuroeng Rehabil. 2024 Nov 5;21(1):197. doi: 10.1186/s12984-024-01480-0.
3
Characterization of normative angular joint kinematics during two functional upper limb tasks.规范化上肢关节角运动学在两种功能性上肢任务中的特征。
Gait Posture. 2019 Mar;69:176-186. doi: 10.1016/j.gaitpost.2019.01.037. Epub 2019 Jan 28.
4
Validation of a 3D Markerless Motion Capture Tool Using Multiple Pose and Depth Estimations for Quantitative Gait Analysis.基于多姿态和深度估计的三维无标记运动捕捉工具在定量步态分析中的验证。
Sensors (Basel). 2024 Nov 5;24(22):7105. doi: 10.3390/s24227105.
5
Validity and Intrarater Reliability of 2-Dimensional Motion Analysis Using a Handheld Tablet Compared to Traditional 3-Dimensional Motion Analysis.与传统三维运动分析相比,使用手持平板电脑进行二维运动分析的有效性和评分者内信度。
J Sport Rehabil. 2015 Jan 22;24(4). doi: 10.1123/jsr.2014-0194. Print 2015 Nov 1.
6
Establishing the Reliability of the GaitON Motion Analysis System: A Foundational Study for Gait and Posture Analysis in a Healthy Population.建立步态 ON 运动分析系统的可靠性:健康人群步态和姿势分析的基础研究。
Sensors (Basel). 2024 Oct 26;24(21):6884. doi: 10.3390/s24216884.
7
Inclusion of a skeletal model partly improves the reliability of lower limb joint angles derived from a markerless depth camera.包含骨骼模型部分提高了基于无标记深度相机的下肢关节角度的可靠性。
J Biomech. 2024 Jun;170:112160. doi: 10.1016/j.jbiomech.2024.112160. Epub 2024 May 22.
8
Validity and Reliability of OpenPose-Based Motion Analysis in Measuring Knee Valgus during Drop Vertical Jump Test.基于 OpenPose 的运动分析测量垂直跳测试中膝关节外翻的有效性和可靠性。
J Sports Sci Med. 2024 Sep 1;23(1):515-525. doi: 10.52082/jssm.2024.515. eCollection 2024 Sep.
9
Using markerless motion capture and musculoskeletal models: An evaluation of joint kinematics.基于无标记运动捕捉和肌肉骨骼模型的关节运动学评估。
Technol Health Care. 2024;32(5):3433-3442. doi: 10.3233/THC-240202.
10
Validation of OpenCap: A low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks.验证 OpenCap:一种用于重返运动任务中下肢运动学的低成本无标记运动捕捉系统。
J Biomech. 2024 Jun;171:112200. doi: 10.1016/j.jbiomech.2024.112200. Epub 2024 Jun 19.

引用本文的文献

1
Development of a MediaPipe-based framework for biomechanical quantification of table tennis forehand strokes.基于MediaPipe的乒乓球正手击球生物力学量化框架的开发。
Front Sports Act Living. 2025 Aug 15;7:1635581. doi: 10.3389/fspor.2025.1635581. eCollection 2025.
2
Using a Webcam to Assess Upper Extremity Proprioception: Experimental Validation and Application to Persons Post Stroke.使用网络摄像头评估上肢本体感觉:实验验证及对中风后患者的应用
Sensors (Basel). 2024 Nov 21;24(23):7434. doi: 10.3390/s24237434.