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

Numerical investigation of radiation-assisted freezing in nanomaterial-enhanced porous enclosures.

Scientific reports·2026
Same author

Investigation of nano-particle effects on cold storage performance using finite element modeling.

Scientific reports·2026
Same author

Computational assessment of cold thermal energy storage improvement using hybrid nanofluid cooling and metal foam media.

Scientific reports·2026
Same author

Numerical assessment of Lorentz-force-driven nanofluid cooling in trapezoidal ducts for improved solar panel performance.

Scientific reports·2026
Same author

Comparative analysis of compressible solver schemes for underexpanded jet aerodynamics with Schlieren validation.

Scientific reports·2026
Same author

Adaptive multi-objective optimization of microgrid energy management using deep reinforcement learning considering battery degradation and renewable uncertainty.

Scientific reports·2026

相关实验视频

Updated: Jul 6, 2025

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
07:52

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

14.2K

以ReliefF为基础的深度神经网络极端学习推进任务识别,实现人工四肢控制.

Luttfi A Al-Haddad1, Wissam H Alawee2, Ali Basem3

  • 1Training and Workshops Center, University of Technology- Iraq, Baghdad, Iraq.

Computers in biology and medicine
|December 28, 2023
PubMed
概括

这项研究介绍了一种新的深度神经网络 (DNN) 与ReliefF算法用于增强人工四肢控制. DNN-ReliefF模型显著提高了假肢任务识别准确度,精度和回忆率.

关键词:
深度学习是一种深度学习.电脑电流信号 电脑电流信号关于MILIMBEEG数据集的研究已经开始.假肢控制系统 假肢控制系统帮助F 帮助F任务识别 任务识别

更多相关视频

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.3K
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

771

相关实验视频

Last Updated: Jul 6, 2025

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
07:52

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

14.2K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.3K
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

771

科学领域:

  • 生物医学工程 生物医学工程
  • 神经科学是一个神经科学.
  • 机器学习 机器学习

背景情况:

  • 在生物医学工程中,对人工肢体进行有效的实时控制至关重要.
  • 目前的假肢控制系统需要改进任务识别能力.

研究的目的:

  • 开发一种先进的方法来增强假肢控制系统中的任务识别.
  • 结合ReliefF基于深度神经网络 (DNN) 的方法来提高性能.

主要方法:

  • 利用了电脑电图 (EEG) 信号的MILimbEEG数据集.
  • 从时间域EEG信号计算的统计特征 (算术平均值,标准偏差,斜率).
  • 使用ReliefF算法进行最高特征选择 (SFS),并将其与DNNs集成.

主要成果:

  • 开发的DNN-ReliefF模型实现了高性能指标:97.4%的准确性,97.3%的精度和97.4%的回忆.
  • 没有SFS的传统DNN模型显示性能明显较低 (约为50.8%).
  • 通过将SFS与ReliefF.结合起来,在任务识别方面取得了显著的改进.

结论:

  • DNN-ReliefF模型代表了一个强大的平台,用于实时假肢控制的进步.
  • ReliefF与DNNs的集成显著提高了假肢系统的有效性.
  • 这种方法对人工四肢控制的未来具有变革性的潜力.