Lower Extremity Inverse Kinematics Results Differ Between Inertial Measurement Unit- and Marker-Derived Gait Data

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,A. Hunt,Ronald F. Zernicke,Russell T. Johnson
出处
期刊:Journal of Applied Biomechanics [International Society of Biomechanics]
卷期号:39 (3): 133-142 被引量:7
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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
September发布了新的文献求助10
刚刚
刚刚
空青完成签到,获得积分10
1秒前
September完成签到,获得积分10
5秒前
奔跑的青霉素完成签到 ,获得积分10
6秒前
黑化小狗发布了新的文献求助10
8秒前
领导范儿应助儒雅老太采纳,获得10
14秒前
16秒前
18秒前
空青发布了新的文献求助10
19秒前
奥雷里亚诺完成签到 ,获得积分10
20秒前
poki完成签到,获得积分10
20秒前
20秒前
香菜芋头完成签到,获得积分10
22秒前
96121abc发布了新的文献求助10
23秒前
田様应助lin采纳,获得10
23秒前
归尘发布了新的文献求助10
23秒前
MutantKitten发布了新的文献求助10
25秒前
GGbond完成签到,获得积分10
27秒前
川絮完成签到,获得积分10
27秒前
33秒前
35秒前
Xmy完成签到,获得积分10
38秒前
xuan2022发布了新的文献求助100
39秒前
42秒前
guo完成签到 ,获得积分10
46秒前
88发布了新的文献求助10
47秒前
48秒前
Akim应助科研通管家采纳,获得30
48秒前
英俊的铭应助科研通管家采纳,获得10
48秒前
思源应助科研通管家采纳,获得10
48秒前
小蘑菇应助科研通管家采纳,获得10
48秒前
48秒前
共享精神应助科研通管家采纳,获得10
48秒前
小二郎应助科研通管家采纳,获得10
48秒前
赘婿应助科研通管家采纳,获得10
48秒前
49秒前
49秒前
49秒前
49秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Various Faces of Animal Metaphor in English and Polish 800
Signals, Systems, and Signal Processing 610
Photodetectors: From Ultraviolet to Infrared 500
On the Dragon Seas, a sailor's adventures in the far east 500
Yangtze Reminiscences. Some Notes And Recollections Of Service With The China Navigation Company Ltd., 1925-1939 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6349688
求助须知:如何正确求助?哪些是违规求助? 8164536
关于积分的说明 17179129
捐赠科研通 5406001
什么是DOI,文献DOI怎么找? 2862330
邀请新用户注册赠送积分活动 1839973
关于科研通互助平台的介绍 1689190