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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.

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/12109401/03e619090209/biomimetics-10-00339-g001.jpg

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