Jove
Visualize
联系我们

相关概念视频

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
Muscles that Move the Leg01:23

Muscles that Move the Leg

The movement of the legs is facilitated by numerous muscles located within the anterior, medial, and posterior compartments of the thigh.
Anterior Compartment
The quadriceps femoris, the most visible muscle of the anterior compartment, is integral for leg extension and thigh flexion. It is formed by merging four distinct muscles — the vastus lateralis, vastus medialis, vastus intermedius, and rectus femoris. The quadriceps tendon, a shared tendon of the four quadriceps muscles, is affixed to...
Muscles of the Leg that Move the Foot and Toes01:28

Muscles of the Leg that Move the Foot and Toes

The human leg comprises an intricate system of muscles that facilitate the movement of feet and toes. Within this system, the muscles are categorized into the anterior, lateral, and posterior compartments, each with a unique set of muscles carrying out specific functions.
Anterior Compartment
The anterior compartment includes muscles that contribute to the dorsiflexion of the foot. This compartment houses the tibialis anterior, extensor hallucis longus, and extensor digitorum longus muscles.

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

Land use dominate the evolution of ecosystem services in the Huaihe River Eco-Economic Belt, China.

Scientific reports·2026
Same author

Long-Term Stability of a Coronary-Encasing Cardiac Paraganglioma: A 4-Year Multimodality Imaging Follow-Up.

Circulation. Cardiovascular imaging·2026
Same author

Dual-driven optimization of collaborative multi-agent via case learning and curiosity.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Artificial intelligence-assisted compressed sensing CINE enhances the workflow of cardiac magnetic resonance in challenging patients.

World journal of cardiology·2025
Same author

A Blast-Resistant NLR Gene Confers Drought Resistance by Competitively Interacting with an E3 Ligase to Protect Phenylalanine Ammonia-Lyase in Rice.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

A novel OsCRK14-OsRLCK57-MAPK signaling module activates OsbZIP66 to confer drought resistance in rice.

Molecular plant·2025
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

相关实验视频

Updated: Jun 23, 2026

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

43.3K

下肢运动识别与改进的SVM基于表面肌电图.

Pengjia Tu1, Junhuai Li2, Huaijun Wang2

  • 1College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种改进的支向量机器 (SVM),用于使用表面电肌图 (sEMG) 信号识别下肢运动. 该方法在健康和膝盖病理患者中实现了高精度,用于外骨机器人康复.

关键词:
在GA-PSO-SVM中使用.下肢运动识别功能 下肢运动识别功能多非线性特征是多非线性特征.非负矩阵因子化的非负矩阵因子化.表面电力学图 (surface electromyography) 是一种表面电力学图.

更多相关视频

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke
08:23

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke

Published on: July 26, 2021

2.5K
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

447

相关实验视频

Last Updated: Jun 23, 2026

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

43.3K
Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke
08:23

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke

Published on: July 26, 2021

2.5K
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

447

科学领域:

  • 生物医学工程 生物医学工程
  • 康复机器人 康复机器人
  • 信号处理 信号处理

背景情况:

  • 识别下肢运动对于外骨机器人辅助康复至关重要,特别是对于膝盖病理患者.
  • 由于个体差异,表面电肌图 (sEMG) 信号的变化对准确的运动识别构成了重大挑战.
  • 现有的方法可能会在下肢运动期间对sEMG数据的复杂性和主体间的变异性进行斗争.

研究的目的:

  • 开发和验证一个改进的基于sEMG的系统,用于准确的下肢运动识别在外骨康复.
  • 为了应对跨主体变异性和复杂的sEMG信号模式的挑战.
  • 为了提高机器人辅助康复的有效性,对于有或没有膝盖病理的个人.

主要方法:

  • 使用非负矩阵因子化 (NMF) 来分析多通道sEMG信号的肌肉协同作用.
  • 提取的多非线性sEMG特征反映了在各种下肢运动期间的肌肉状态复杂性.
  • 采用费舍尔判别函数来进行特征选择和维度减小,使用混合遗传算法-粒子群优化 (GA-PSO) 方法优化了SVM参数.

主要成果:

  • 提出的基于sEMG的SVM方法实现了三种下肢运动的高识别精度.
  • 健康人群的平均准确率达到96.03%,膝盖病理患者的平均准确率达到93.65%.
  • 证明了该方法在区分不同学科组的运动中的有效性和可行性.

结论:

  • 开发的基于sEMG的运动识别系统显示了对推进外骨机器人辅助康复的重大前景.
  • 改进的SVM方法有效地处理sEMG信号的变化,增强诊断和治疗能力.
  • 该方法为实时下肢运动识别提供了可靠的解决方案,改善了各种患者群体的康复结果.