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用于观测人体关节运动的低数量 IMU 估计的新方法。

New Method for Reduced-Number IMU Estimation in Observing Human Joint Motion.

机构信息

Faculty of Transportation Mechanical Engineering, The University of Danang-University of Science and Technology, Danang 550000, Vietnam.

Department of Vehicle Engineering, National Taipei University of Technology, Taipei 106344, Taiwan.

出版信息

Sensors (Basel). 2023 Jun 19;23(12):5712. doi: 10.3390/s23125712.

DOI:10.3390/s23125712
PMID:37420876
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10302940/
Abstract

Observation of human joint motion plays an important role in many fields. The results of the human links can provide information about musculoskeletal parameters. Some devices can track real-time joint movement in the human body during essential daily activities, sports, and rehabilitation with memory for storing the information concerning the body. Based on the algorithm for signal features, the collected data can reveal the conditions of multiple physical and mental health issues. This study proposes a novel method for monitoring human joint motion at a low cost. We propose a mathematical model to analyze and simulate the joint motion of a human body. The model can be applied to an Inertial Measurement Unit (IMU) device for tracking dynamic joint motion of a human. Finally, the combination of image-processing technology was used to verify the results of model estimation. Moreover, the verification showed that the proposed method can estimate joint motions properly with reduced-number IMUs.

摘要

观察人体关节运动在许多领域都起着重要作用。人体关节的运动结果可以提供有关肌肉骨骼参数的信息。一些设备可以在日常基本活动、运动和康复期间实时跟踪人体关节的运动，并具有存储有关身体信息的记忆功能。基于信号特征的算法，所收集的数据可以揭示多种身心健康问题的状况。本研究提出了一种低成本监测人体关节运动的新方法。我们提出了一种数学模型来分析和模拟人体关节运动。该模型可应用于惯性测量单元（IMU）设备，用于跟踪人体动态关节运动。最后，使用图像处理技术对模型估计的结果进行了验证。此外，验证表明，该方法可以通过使用较少数量的 IMU 来正确地估计关节运动。

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