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

After Skin Wounding, Noncoding dsRNA Coordinates Prostaglandins and Wnts to Promote Regeneration.

The Journal of investigative dermatology·2017
Same author

Metabolic profiling and novel plasma biomarkers for predicting survival in epithelial ovarian cancer.

Oncotarget·2017
Same author

Identification of a six-lncRNA signature associated with recurrence of ovarian cancer.

Scientific reports·2017
Same author

Ropivacaine versus levobupivacaine in peripheral nerve block: A PRISMA-compliant meta-analysis of randomized controlled trials.

Medicine·2017
Same author

Fabrication of fluorescent composite hydrogel using in situ synthesis of upconversion nanoparticles.

Nanotechnology·2017
Same author

Ammonium assimilation: An important accessory during aerobic denitrification of Pseudomonas stutzeri T13.

Bioresource technology·2017

相关实验视频

Updated: May 17, 2025

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
06:25

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

8.4K

基于使用渐进特征选择方法的sEMG信号进行步行识别.

Chuanjiang Li1, Xinhao Ding1, Jiajun Tu1

  • 1The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200233, China.

Journal of neuroscience methods
|May 8, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种渐进特征选择 (PFS) 方法用于表面肌电图 (sEMG) 步态识别,实现外骨控制的高精度. PFS方法有效地减少了冗余的功能,提高了识别性能和安全性.

关键词:
3D动态捕捉 3D动态捕获步态识别系统可以识别步态.渐进特征选择 (PFS) 是一种逐渐的特征选择.表面电肌图 (sEMG) 是指表面电肌图.

更多相关视频

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

312
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K

相关实验视频

Last Updated: May 17, 2025

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
06:25

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

8.4K
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

312
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K

科学领域:

  • 生物医学工程 生物医学工程
  • 机器人技术 机器人技术 机器人技术
  • 信号处理 信号处理

背景情况:

  • 使用表面电肌图 (sEMG) 的步态识别对于控制外骨架设备至关重要.
  • 目前的研究面临的挑战是特征提取和识别精度,由于无关和冗余的sEMG特征.

研究的目的:

  • 开发一种有效的特征选择方法,用于基于sEMG的步态识别.
  • 为了提高下肢外骨应用的步态识别的准确性和效率.

主要方法:

  • 提出了一种新的渐进特征选择 (PFS) 方法,集成立体模型投影和3D动态捕获,从下肢肌肉中提取步行阶段特定的特征.
  • 时间和频率域特征被捕获和优化,使用基于健身评估的渐进特征组合来消除不那么有信息的特征.

主要成果:

  • PFS方法在sEMG步态识别方面表现出很高的表现,平均准确率为98.54%,中位准确率为98.67%.
  • 对实验和SIAT-LLMD数据集的验证显示,PFS算法达到高达98.91%的准确性,超过了最先进的方法.

结论:

  • 拟议的PFS方法有效地减少了特征维度,从而提高了步态识别的准确性.
  • 提高识别精度和减少特征冗余性有助于提高下肢外骨架机器人的安全性.