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尝试使用九轴运动传感器制作客观 Fugl-Meyer 上肢评估项目。

Attempt to Make the Upper-Limb Item of Objective Fugl-Meyer Assessment Using 9-Axis Motion Sensors.

机构信息

Department of Occupational Therapy and Rehabilitation, Itami Kousei Neurosurgical Hospital, Itami City 664-0028, Japan.

Department of Occupational Therapy, School of Comprehensive Rehabilitation, Osaka Metropolitan University, Habikino City 558-8555, Japan.

出版信息

Sensors (Basel). 2023 May 30;23(11):5213. doi: 10.3390/s23115213.

DOI:10.3390/s23115213
PMID:37299941
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10255665/
Abstract

The Fugl-Meyer Assessment (FMA) has been used as a functional assessment of upper-limb function in stroke patients. This study aimed to create a more objective and standardized evaluation based on an FMA of the upper-limb items. A total of 30 first-ever stroke patients (65.3 ± 10.3 years old) and 15 healthy participants (35.4 ± 13.4 years old) admitted to Itami Kousei Neurosurgical Hospital were included. A nine-axis motion sensor was attached to the participants, and the joint angles of 17 upper-limb items (excluding fingers) and 23 FMA upper-limb items (excluding reflexes and fingers) were measured. From the measurement results, we analyzed the time-series data of each movement and obtained the correlation between the joint angles of each part. Discriminant analysis showed that 17 and 6 items had a concordance rate of ≥80% (80.095.6%) and <80% (64.475.6%), respectively. In the multiple regression analysis of continuous variables of FMA, a good regression model was obtained to predict the FMA with three to five joint angles. The discriminant analysis for 17 evaluation items suggests the possibility of roughly calculating FMA scores from joint angles.

摘要

Fugl-Meyer 评估(FMA)已被用作脑卒中患者上肢功能的功能评估。本研究旨在基于 FMA 上肢项目创建一种更客观和标准化的评估方法。共有 30 名首次脑卒中患者(65.3±10.3 岁)和 15 名健康参与者(35.4±13.4 岁)入住伊丹光星神经外科医院。将九轴运动传感器附着在参与者身上,测量了 17 个上肢项目(不包括手指)和 23 个 FMA 上肢项目(不包括反射和手指)的关节角度。从测量结果中,我们分析了每个运动的时间序列数据,并获得了每个部位关节角度之间的相关性。判别分析表明,17 项和 6 项的一致性率分别为≥80%(80.095.6%)和<80%(64.475.6%)。在 FMA 连续变量的多元回归分析中,获得了一个很好的回归模型,可以用三个到五个关节角度来预测 FMA。17 个评估项目的判别分析表明,从关节角度大致计算 FMA 分数是可能的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/c53cf3edaa00/sensors-23-05213-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/d8678a8356cb/sensors-23-05213-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/cf68f5cdbe76/sensors-23-05213-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/9d7f144c2291/sensors-23-05213-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/b6df3eb32b4b/sensors-23-05213-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/c53cf3edaa00/sensors-23-05213-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/d8678a8356cb/sensors-23-05213-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/cf1950efa15d/sensors-23-05213-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/cf68f5cdbe76/sensors-23-05213-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/fd3a1f82a2d3/sensors-23-05213-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/9d7f144c2291/sensors-23-05213-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/b6df3eb32b4b/sensors-23-05213-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501e/10255665/c53cf3edaa00/sensors-23-05213-g007.jpg

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