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

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Andres Emilio Hurtado-Perez, Manuel Toledano-Ayala, Irving A Cruz-Albarran, Alejandra Lopez-Zúñiga, Jesús Adrián Moreno-Perez, Alejandra Álvarez-López, Juvenal Rodriguez-Resendiz, Carlos A Perez-Ramirez
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引用次数: 0

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.

运动数据分析中获取和处理策略的技术应用
本文对人体运动分析中用于获取和处理动力学和运动学变量的技术和方法进行了深入的研究。本文分析了运动捕捉相机(mcc)、惯性测量单元(imu)、力平台和其他原型技术的能力和局限性。先进的处理技术,包括过滤和转换方法,以及人工智能(AI)和机器学习(ML)在数据分类中的日益集成的作用也进行了讨论。这些进步提高了生物力学分析的精度和效率,为更准确地评估人类运动模式铺平了道路。这篇综述最后为这些技术在临床和研究环境中的有效应用提供了指南,强调了全面验证以确保可靠性的必要性。这篇全面的综述为生物力学领域的研究人员和专业人员提供了宝贵的资源,指导了人类运动分析的适当技术和方法的选择和应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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