• 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

用于运动数据分析的采集与处理策略的技术应用

Use of Technologies for the Acquisition and Processing Strategies for Motion Data Analysis.

作者信息

Hurtado-Perez Andres Emilio, Toledano-Ayala Manuel, Cruz-Albarran Irving A, Lopez-Zúñiga Alejandra, Moreno-Perez Jesús Adrián, Álvarez-López Alejandra, Rodriguez-Resendiz Juvenal, Perez-Ramirez Carlos A

机构信息

Division de Estudios de Posgrado, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, Querétaro 76010, Mexico.

Tequexquite, Centro de Investigación y Desarrollo Tecnológico para la Accesibilidad e Innovación Social, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Campus Aeropuerto, Carretera a Chichimequillas S/N, Ejido Bolaños, Querétaro 76140, Mexico.

出版信息

Biomimetics (Basel). 2025 May 20;10(5):339. doi: 10.3390/biomimetics10050339.

DOI:10.3390/biomimetics10050339
PMID:40422169
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12109401/
Abstract

This review provides an in-depth examination of the technologies and methods used for the acquisition and processing of kinetic and kinematic variables in human motion analysis. This review analyzes the capabilities and limitations of motion-capture cameras (MCCs), inertial measurement units (IMUs), force platforms, and other prototype technologies. The role of advanced processing techniques, including filtering and transformation methods, and the increasing integration of artificial intelligence (AI) and machine learning (ML) for data classification is also discussed. These advancements enhance the precision and efficiency of biomechanical analyses, paving the way for more accurate assessments of human movement patterns. The review concludes by providing guidelines for the effective application of these technologies in both clinical and research settings, emphasizing the need for comprehensive validation to ensure reliability. This comprehensive overview serves as a valuable resource for researchers and professionals in the field of biomechanics, guiding the selection and application of appropriate technologies and methodologies for human movement analysis.

摘要

本综述深入探讨了在人体运动分析中用于获取和处理动力学与运动学变量的技术和方法。本综述分析了运动捕捉相机（MCC）、惯性测量单元（IMU）、力平台及其他原型技术的能力和局限性。还讨论了先进处理技术的作用，包括滤波和变换方法，以及人工智能（AI）和机器学习（ML）在数据分类方面日益增加的整合应用。这些进展提高了生物力学分析的精度和效率，为更准确地评估人体运动模式铺平了道路。综述最后给出了在临床和研究环境中有效应用这些技术的指导方针，强调了进行全面验证以确保可靠性的必要性。这一全面概述为生物力学领域的研究人员和专业人员提供了宝贵资源，指导他们选择和应用适用于人体运动分析的技术和方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/2f781111e0a0/biomimetics-10-00339-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/03e619090209/biomimetics-10-00339-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/dfaac8ef8697/biomimetics-10-00339-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/d8f5af490822/biomimetics-10-00339-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/1abaec15472f/biomimetics-10-00339-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/73f44fe69cbc/biomimetics-10-00339-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/3d3b98ac39fd/biomimetics-10-00339-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/81f25cdc5a10/biomimetics-10-00339-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/b6868ab08183/biomimetics-10-00339-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/561d3445f059/biomimetics-10-00339-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/0eac165ad60f/biomimetics-10-00339-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/be9a36af3fd2/biomimetics-10-00339-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/67bd7af7893f/biomimetics-10-00339-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/2f781111e0a0/biomimetics-10-00339-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/03e619090209/biomimetics-10-00339-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/dfaac8ef8697/biomimetics-10-00339-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/d8f5af490822/biomimetics-10-00339-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/1abaec15472f/biomimetics-10-00339-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/73f44fe69cbc/biomimetics-10-00339-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/3d3b98ac39fd/biomimetics-10-00339-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/81f25cdc5a10/biomimetics-10-00339-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/b6868ab08183/biomimetics-10-00339-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/561d3445f059/biomimetics-10-00339-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/0eac165ad60f/biomimetics-10-00339-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/be9a36af3fd2/biomimetics-10-00339-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/67bd7af7893f/biomimetics-10-00339-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8eb/12109401/2f781111e0a0/biomimetics-10-00339-g013.jpg

相似文献

1
Use of Technologies for the Acquisition and Processing Strategies for Motion Data Analysis.用于运动数据分析的采集与处理策略的技术应用
Biomimetics (Basel). 2025 May 20;10(5):339. doi: 10.3390/biomimetics10050339.
2
The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.人工智能与可穿戴惯性测量单元在医学中的应用：系统评价
JMIR Mhealth Uhealth. 2025 Jan 29;13:e60521. doi: 10.2196/60521.
3
The Validity and Usability of Markerless Motion Capture and Inertial Measurement Units for Quantifying Dynamic Movements.无标记运动捕捉和惯性测量单元在量化动态运动方面的有效性和实用性。
Med Sci Sports Exerc. 2025 Mar 1;57(3):641-655. doi: 10.1249/MSS.0000000000003579. Epub 2024 Oct 9.
4
Artificial Neural Network Approach to Guarantee the Positioning Accuracy of Moving Robots by Using the Integration of IMU/UWB with Motion Capture System Data Fusion.人工神经网络方法通过融合惯性测量单元/超宽带与运动捕捉系统数据，保证移动机器人的定位精度。
Sensors (Basel). 2022 Jul 31;22(15):5737. doi: 10.3390/s22155737.
5
Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network.超越步态实验室解锁步态分析：使用惯性运动单元和卷积神经网络对膝关节运动学进行高保真复制。
Arthroplast Today. 2025 Apr 15;33:101656. doi: 10.1016/j.artd.2025.101656. eCollection 2025 Jun.
6
Can We Accurately Measure Axial Segment Coordination during Turning Using Inertial Measurement Units (IMUs)?使用惯性测量单元（IMUs）能否准确测量转身过程中的轴向节段协调性？
Sensors (Basel). 2020 Apr 29;20(9):2518. doi: 10.3390/s20092518.
7
Optimal control simulations tracking wearable sensor signals provide comparable running gait kinematics to marker-based motion capture.跟踪可穿戴传感器信号的最优控制模拟可提供与基于标记的运动捕捉相当的跑步步态运动学。
PeerJ. 2025 Mar 6;13:e19035. doi: 10.7717/peerj.19035. eCollection 2025.
8
Exploring the Role of Artificial Intelligence (AI)-Driven Training in Laparoscopic Suturing: A Systematic Review of Skills Mastery, Retention, and Clinical Performance in Surgical Education.探索人工智能驱动的培训在腹腔镜缝合中的作用：外科教育中技能掌握、保留和临床性能的系统评价
Healthcare (Basel). 2025 Mar 6;13(5):571. doi: 10.3390/healthcare13050571.
9
Validity of Valor Inertial Measurement Unit for Upper and Lower Extremity Joint Angles.Valor 惯性测量单元对上、下肢关节角度测量的有效性。
Sensors (Basel). 2024 Sep 8;24(17):5833. doi: 10.3390/s24175833.
10
Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review.基于可穿戴惯性传感器的地面反作用力和反力矩的间接测量：系统综述。
Sensors (Basel). 2018 Aug 5;18(8):2564. doi: 10.3390/s18082564.

本文引用的文献

1
Learning based lower limb joint kinematic estimation using open source IMU data.基于开源惯性测量单元（IMU）数据的下肢关节运动学学习估计
Sci Rep. 2025 Feb 12;15(1):5287. doi: 10.1038/s41598-025-89716-4.
2
Reliability of a Low-Cost Inertial Measurement Unit (IMU) to Measure Punch and Kick Velocity.一种低成本惯性测量单元（IMU）测量拳打和踢腿速度的可靠性
Sensors (Basel). 2025 Jan 7;25(2):307. doi: 10.3390/s25020307.
3
Reliability of an Inertial Measurement System Applied to the Technical Assessment of Forehand and Serve in Amateur Tennis Players.
应用于业余网球运动员正手击球和发球技术评估的惯性测量系统的可靠性
Bioengineering (Basel). 2025 Jan 2;12(1):30. doi: 10.3390/bioengineering12010030.
4
DeepLabCut custom-trained model and the refinement function for gait analysis.用于步态分析的深度实验室切割定制训练模型及优化功能。
Sci Rep. 2025 Jan 18;15(1):2364. doi: 10.1038/s41598-025-85591-1.
5
Skiing quality analysis of recreational skiers based on IMU data and self-assessment.基于惯性测量单元（IMU）数据和自我评估的休闲滑雪者滑雪质量分析
Front Sports Act Living. 2024 Dec 24;6:1495176. doi: 10.3389/fspor.2024.1495176. eCollection 2024.
6
Multi-Planar Cervical Motion Dataset: IMU Measurements and Goniometer.多平面颈椎运动数据集：惯性测量单元测量与角度计
Sci Data. 2025 Jan 3;12(1):13. doi: 10.1038/s41597-024-04351-4.
7
Computer vision-based instantaneous speed tracking system for measuring the subtask speed in the 100-meter sprinter: Development and concurrent validity study.基于计算机视觉的100米短跑运动员子任务速度测量瞬时速度跟踪系统：开发与同时效度研究
Heliyon. 2024 Jan 6;10(2):e24086. doi: 10.1016/j.heliyon.2024.e24086. eCollection 2024 Jan 30.
8
Measuring Changes in Upper Body Movement Due to Fasting Using a Camera.使用摄像头测量禁食引起的上半身运动变化。
Sensors (Basel). 2024 Nov 13;24(22):7242. doi: 10.3390/s24227242.
9
Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment.利用惯性测量单元检测多条件步态实验中的动态稳定性变化。
Sensors (Basel). 2024 Oct 31;24(21):7044. doi: 10.3390/s24217044.
10
IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms.单板滑雪 U 型池空中时间检测：U-Net 深度学习方法优于传统阈值算法。
Sensors (Basel). 2024 Oct 22;24(21):6773. doi: 10.3390/s24216773.