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验证一种惯性传感器算法,以量化健康年轻成年人和轻度创伤性脑损伤个体的头部和躯干运动。

Validation of an Inertial Sensor Algorithm to Quantify Head and Trunk Movement in Healthy Young Adults and Individuals with Mild Traumatic Brain Injury.

机构信息

Department of Neurology, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA.

VA Portland Health Care System, 3710 SW US Veterans Hospital Road, Portland, OR 97239, USA.

出版信息

Sensors (Basel). 2018 Dec 19;18(12):4501. doi: 10.3390/s18124501.

DOI:10.3390/s18124501
PMID:30572640
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6308527/
Abstract

Wearable inertial measurement units (IMUs) may provide useful, objective information to clinicians interested in quantifying head movements as patients' progress through vestibular rehabilitation. The purpose of this study was to validate an IMU-based algorithm against criterion data (motion capture) to estimate average head and trunk range of motion (ROM) and average peak velocity. Ten participants completed two trials of standing and walking tasks while moving the head with and without moving the trunk. Validity was assessed using a combination of Intra-class Correlation Coefficients (ICC), root mean square error (RMSE), and percent error. Bland-Altman plots were used to assess bias. Excellent agreement was found between the IMU and criterion data for head ROM and peak rotational velocity (average ICC > 0.9). The trunk showed good agreement for most conditions (average ICC > 0.8). Average RMSE for both ROM (head = 2.64°; trunk = 2.48°) and peak rotational velocity (head = 11.76 °/s; trunk = 7.37 °/s) was low. The average percent error was below 5% for head and trunk ROM and peak rotational velocity. No clear pattern of bias was found for any measure across conditions. Findings suggest IMUs may provide a promising solution for estimating head and trunk movement, and a practical solution for tracking progression throughout rehabilitation or home exercise monitoring.

摘要

可穿戴惯性测量单元(IMU)可为有兴趣量化头部运动的临床医生提供有用的客观信息,因为患者在进行前庭康复治疗时会进行头部运动。本研究的目的是使用基于 IMU 的算法与标准数据(运动捕捉)进行验证,以估计平均头部和躯干运动范围(ROM)和平均峰值速度。10 名参与者在站立和行走任务中完成了两次试验,在头部运动时和不运动时移动躯干。使用组内相关系数(ICC)、均方根误差(RMSE)和百分比误差的组合来评估有效性。Bland-Altman 图用于评估偏差。IMU 和标准数据之间在头部 ROM 和峰值旋转速度方面具有很好的一致性(平均 ICC > 0.9)。对于大多数条件,躯干的一致性也很好(平均 ICC > 0.8)。ROM(头部=2.64°;躯干=2.48°)和峰值旋转速度(头部=11.76°/s;躯干=7.37°/s)的平均 RMSE 较低。头部和躯干 ROM 以及峰值旋转速度的平均百分比误差均低于 5%。在任何条件下,任何测量值均未发现明显的偏差模式。研究结果表明,IMU 可能是一种很有前途的解决方案,可以估计头部和躯干的运动,并且是在康复过程中或在家中进行运动监测时跟踪进展的实用解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5550/6308527/2020d7804fd0/sensors-18-04501-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5550/6308527/a2a6de94a866/sensors-18-04501-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5550/6308527/83f90eb33f70/sensors-18-04501-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5550/6308527/2020d7804fd0/sensors-18-04501-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5550/6308527/a2a6de94a866/sensors-18-04501-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5550/6308527/83f90eb33f70/sensors-18-04501-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5550/6308527/2020d7804fd0/sensors-18-04501-g003.jpg

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