Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

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

Speed-Dependent Turning Strategies in Quadrupedal Locomotion: Insights from Computational Modeling.

bioRxiv : the preprint server for biology·2026
Same author

Tumor agnostic drug delivery with dynamic nanohydrogels.

Nature communications·2026
Same author

µCodes: A Universal Grid Platform for Microscale Mapping, Microscopy Navigation, and Multimodal Imaging.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Ionic Mechanisms Underlying Bistability in Spinal Motoneurons: Insights from a Computational Model.

bioRxiv : the preprint server for biology·2025
Same author

How advocacy groups on Twitter and media coverage can drive US firearm acquisition: A causal study.

PNAS nexus·2025
Same author

Synergistic Torso-Muscle-Controlled Detached Robotic Hand: A Novel Approach for Post-Stroke Hand Rehabilitation.

medRxiv : the preprint server for health sciences·2025

相关实验视频

Updated: Jun 11, 2025

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
04:49

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

663

使用机器人辅助中风康复来分类残留中风严重程度:机器学习方法.

Russell Jeter1,2, Raymond Greenfield1, Stephen N Housley2,3

  • 1Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, United States.

JMIR biomedical engineering
|October 7, 2024
PubMed
概括

这项研究引入了一种机器学习模型,用于使用家庭机器人康复数据进行自主性中风严重程度分类. 光梯度增强模型实现了96.70%的准确性,增强了个性化的中风恢复.

关键词:
人工智能的人工智能是人工智能.机器学习是机器学习.神经可塑性 神经可塑性物理治疗疗法 物理治疗疗法康复 机器人 康复 机器人一次性中风中风中风中风中风

更多相关视频

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.2K
Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies
05:28

Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies

Published on: October 11, 2024

506

相关实验视频

Last Updated: Jun 11, 2025

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
04:49

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

663
Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.2K
Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies
05:28

Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies

Published on: October 11, 2024

506

科学领域:

  • 在康复科学中使用机器人和机器学习
  • 神经康复工程 神经康复工程
  • 临床生物力学 临床生物力学

背景情况:

  • 脑卒中康复传统上是在临床环境中进行的,但越来越多的人倾向于在家进行技术综合康复.
  • 这项研究通过结合机器人和机器学习来支持自主,家庭中风恢复.

研究的目的:

  • 开发监督机器学习方法,使用家庭动力学数据对中风残留严重程度进行分类.
  • 为了提高中风康复自主分类的准确性.

主要方法:

  • 33名中风患者使用Motus Nova机器人进行家庭上下肢治疗,收集运动,辅助和活动数据.
  • 数据被处理并与临床医生定义的中风严重程度标签配对 (没有ROM,低ROM,高ROM).
  • 四个机器学习算法 (光梯度增强,额外树木分类器,深度前神经网络,物流回归) 被训练并使用80:20数据分割和10倍交叉验证进行评估.

主要成果:

  • 光梯度增强 (LGB) 模型表现出卓越的性能,其96.70%的F1得分用于自主检测中风严重程度.
  • 包含139个决策树的LGB模型显著超过了后勤回归 (55.82%),额外树分类器 (94.81%) 和深度前神经网络 (70.11%).

结论:

  • 客观的康复数据与机器学习相结合,可以有效地分类残留中风严重程度.
  • 经过训练的模型,利用会话总结统计数据,有可能实时集成到临床环境中,如门诊设施,以增强个性化的中风康复.