惯性测量装置
步态
运动捕捉
运动学
跨步
步态分析
物理医学与康复
反向动力学
矢状面
人工智能
计算机科学
医学
运动(物理)
解剖
物理
经典力学
机器人
作者
Jocelyn F. Hafer,Julien A Mihy,Andrew P. Hunt,Ronald F. Zernicke,Russell T. Johnson
标识
DOI:10.1123/jab.2022-0194
摘要
In-lab, marker-based gait analyses may not represent real-world gait. Real-world gait analyses may be feasible using inertial measurement units (IMUs) in combination with open-source data processing pipelines (OpenSense). Before using OpenSense to study real-world gait, we must determine whether these methods estimate joint kinematics similarly to traditional marker-based motion capture (MoCap) and differentiate groups with clinically different gait mechanics. Healthy young and older adults and older adults with knee osteoarthritis completed this study. We captured MoCap and IMU data during overground walking at 2 speeds. MoCap and IMU kinematics were computed with OpenSim workflows. We tested whether sagittal kinematics differed between MoCap and IMU, whether tools detected between-group differences similarly, and whether kinematics differed between tools by speed. MoCap showed more anterior pelvic tilt (0%–100% stride) and joint flexion than IMU (hip: 0%–38% and 61%–100% stride; knee: 0%–38%, 58%–89%, and 95%–99% stride; and ankle: 6%–99% stride). There were no significant tool-by-group interactions. We found significant tool-by-speed interactions for all angles. While MoCap- and IMU-derived kinematics differed, the lack of tool-by-group interactions suggests consistent tracking across clinical cohorts. Results of the current study suggest that IMU-derived kinematics with OpenSense may enable reliable evaluation of gait in real-world settings.
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