Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

Search research articles

相关实验视频

Updated: Jan 11, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

用混合增强的LSTM自编码器和可穿戴传感器数据估计Fugl-Meyer评估上端子分数.

Minghao Liu¹, Hsuan-Yu Lu², Shuk-Fan Tong¹

¹Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.

Sensors (Basel, Switzerland)

|November 13, 2025

概括

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

A Portable Soft Robotic Glove with Fully Functional Thumb Assistance for Complex Dexterous Fine Motor Skills.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

CIITA/PRMT5 promote CD4<sup>+</sup>Gzma<sup>+</sup> T cell activation via H3R2me2-mediated endothelial expression of MHC class II in smoking-induced atherosclerosis.

BMC medicine·2026

Same author

Effect of Calcareous Material Particle Size via Separate Grinding on the Burnability and Microstructure Development of Portland Cement Clinker.

Materials (Basel, Switzerland)·2026

Same author

Prompting Rain Off: Evolving Compact Dual Prompts for Continual De-Raining.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Construction and Application of a Tactile Somatosensory Comfort Model for Scrubbing Tasks.

Biomimetics (Basel, Switzerland)·2026

Same author

Identification of herbal formula Huatanqushihuoxue formula as a potential therapeutic agent for metabolism-related fatty liver disease: a multi-omics and network pharmacology approach.

Frontiers in immunology·2026

这项研究开发了一个深度学习模型,使用可穿戴传感器自动估计上肢Fugl-Meyer评估 (FMA-UE) 评分. 这项创新提供了一种更快,更容易使用的方法来评估中风后的运动功能.

科学领域:

生物医学工程生物医学工程
康复科学康复科学康复科学
医疗保健中的人工智能

背景情况:

脑卒中显著影响运动功能,需要进行准确的康复评估.
上肢的Fugl-Meyer评估 (FMA-UE) 是一种标准的但耗时的临床工具.
目前使用传感器和人工智能的远程评估方法在详细的运动功能得分方面面临挑战.

研究的目的:

引入一个深度学习框架,用于自动估计FMA-UE总和分区分数.
为了使中风幸存者能够更频繁和更容易获得的运动功能评估.
克服FMA-UE传统评估在时间和专业知识方面的局限性.

主要方法:

收集了15名参与者的数据,使用四个惯性测量单位 (IMU) 在手臂和干部.
开发了一个集成基于LSTM的自动编码器和混合增强的深度学习模型.
使用一个离开一个主体的交叉验证 (LOSOCV) 来进行可靠的绩效评估.

主要成果:

该模型在所有FMA-UE子部件 (A-D) 中实现了R²值>0.82和Pearson的r>0.90.
标准化平方根平均误差 (NRMSE) 在子部分<0.14,在FMA-UE总分数<0.0678.

关键词:

弗格尔·梅耶尔的评估在IMU,IMU是IMU.深度学习是一种深度学习.远程评估远程评估远程评估一次性中风中风中风中风中风

更多相关视频

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

相关实验视频

Last Updated: Jan 11, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

在估计详细的运动功能得分方面表现出强大的概括性和稳定性.

结论:

一个简洁的,基于传感器的评估框架可以可靠地预测详细的FMA-UE运动功能得分.
这种方法提高了中风运动功能评估的可访问性和效率.
开发的深度学习模型显示了远程和自动化康复监控的巨大潜力.