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

A Contrastive Representation Learning Method for Event Classification in Φ-OTDR Systems.

Sensors (Basel, Switzerland)·2025
Same author

Formononetin Alleviates the Inflammatory Response Induced by Carotid Balloon Injury in Rats via the PP2A/MAPK Axis.

Immunological investigations·2025
Same author

Phenolic Components and Biological Activity of Pomegranate.

Chemistry & biodiversity·2024
Same author

Multifractal Characteristics Analysis of Spatial State of Prefecture-Level Cities in China.

Applied spatial analysis and policy·2023
Same author

ABCB-mediated shootward auxin transport feeds into the root clock.

EMBO reports·2023
Same author

Derivation of the toxicological threshold of silicon element in the extractables and leachables from the pharmaceutical packaging and process components.

Toxicology and industrial health·2022

相关实验视频

Updated: Jan 16, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.2K

高精度下肢意图识别:KPCA-ISSA-SVM方法与sEMG-IMU传感器融合

Kaiyang Yin1, Pengchao Hao1, Huanli Zhao1

  • 1School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China.

Biomimetics (Basel, Switzerland)
|September 26, 2025
PubMed
概括

本研究介绍了一种使用内核主要组件分析 (KPCA) 和改进的子搜索算法 (ISSA) 的新框架,以优化支持矢量机器 (SVM) 来识别从生理信号中的人类运动意图.

关键词:
在KPCA-ISSA-SVM中使用.人类机器康复器件是人类机器康复器件.运动意图识别 运动意图识别机器学习是机器学习.非线性维度缩小的非线性维度缩小在 sEMG-IMU 融合过程中,

更多相关视频

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K
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

1.7K

相关实验视频

Last Updated: Jan 16, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.2K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K
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

1.7K

科学领域:

  • 生物医学工程 生物医学工程
  • 康复技术 康复技术 康复技术
  • 人机界面 人机界面

背景情况:

  • 从生理信号解读人类运动意图对于先进的康复设备至关重要.
  • 传统方法与生物数据的非线性动态作斗争,限制了准确性.
  • 准确的意图识别是对外骨和假肢直观控制的关键.

研究的目的:

  • 为下肢运动意图识别开发一种新且准确的框架.
  • 解决传统方法在捕获复杂的生物数据动态方面的局限性.
  • 提高用于康复应用的人机系统的性能.

主要方法:

  • 集成核心主要组件分析 (KPCA) 用于非线性维度缩小.
  • 采用了改进的搜索算法 (ISSA) 来优化支持向量机 (SVM) 的超参数.
  • 使用同步表面电肌图 (sEMG) 和惯性测量单元 (IMU) 数据进行特征提取.

主要成果:

  • 拟议的KPCA-ISSA-SVM框架实现了95.35%的离线和93.3%的在线识别准确度.
  • 与传统的PCA-SVM (91.85%) 和独立的SVM (89.76%) 相比,表现出更高的性能.
  • 有效处理sEMG-IMU数据和复杂运动模式的非线性合特性.

结论:

  • KPCA-ISSA-SVM架构为意图感知提供了一个强大的,更准确的解决方案.
  • 这一框架促进了更直观,更有效的康复技术的发展.
  • 该研究强调了先进的机器学习技术在解码复杂的生理信号方面的潜力.