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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101

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

Dual-Modulus Microcone Array for Graded Tactile Sensing and Intelligent Slip Detection.

ACS applied materials & interfaces·2026
Same author

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026
Same author

Advances in printable flexible and stretchable thin-film electrodes: materials, interfaces, technologies and bioelectronic applications.

Nanoscale·2026
Same author

Nondestructive determination of ash content in wheat flour via terahertz time-domain spectroscopy.

Frontiers in plant science·2026
Same author

Resting-state brain network alterations in adolescent idiopathic scoliosis using functional near-infrared spectroscopy.

Biomedical engineering online·2026
Same author

A Novel Sliding Mode Differentiator-Based Feature for EMG-Based Hand Gesture Characterization.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

相关实验视频

Updated: Sep 14, 2025

Visualizing Motion Patterns in Acupuncture Manipulation
08:18

Visualizing Motion Patterns in Acupuncture Manipulation

Published on: July 16, 2016

8.9K

一个基于NMF的非欧几里德适应特征提取方案用于肢体运动模式解码在模式识别系统.

Frank Kulwa, Pengrui Tai, Doreen S Sarwatt

    IEEE transactions on bio-medical engineering
    |July 23, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种用于电肌图 (EMG) 信号的新型无监督特征提取方法. 新技术显著提高了动力意图解码的准确性和稳定性,超过了现有的方法.

    更多相关视频

    Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation
    08:27

    Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

    Published on: October 28, 2021

    2.9K
    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

    43.5K

    相关实验视频

    Last Updated: Sep 14, 2025

    Visualizing Motion Patterns in Acupuncture Manipulation
    08:18

    Visualizing Motion Patterns in Acupuncture Manipulation

    Published on: July 16, 2016

    8.9K
    Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation
    08:27

    Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

    Published on: October 28, 2021

    2.9K
    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

    43.5K

    科学领域:

    • 生物医学工程 生物医学工程
    • 康复工程 康复工程 康复工程
    • 信号处理 信号处理

    背景情况:

    • 电肌图 (EMG) 的特征提取对于假肢和辅助设备中的运动意图解码至关重要.
    • 现有的特征提取方法往往患有低解码性能,易受数据集漂移的影响.
    • 训练测试数据漂移对特征提取性能的影响在当前的评估中经常被忽视.

    研究的目的:

    • 为基于EMG的模式识别系统提出一种新的无监督特征提取方案.
    • 通过减少数据集漂移和提高解码精度来解决现有方法的局限性.
    • 提高EMG控制系统的可靠性和稳定性,用于临床和商业应用.

    主要方法:

    • 开发了一个无监督的特征提取方案,利用非负矩阵因子化 (NMF) 和里曼运算来进行特征适应.
    • 实现数据分布对齐,以尽量减少培训和测试数据集之间的漂移.
    • 对截肢者和有能力参与者的13个手和手指运动进行了评估.

    主要成果:

    • 与现有技术相比,实现了显著更高的运动意图解码性能 (p < 0.05).
    • 实现了高平均准确率:截肢者为99.91 ± 0.35%,健身者为99.99 ± 0.02%.
    • 在各种信号噪声比率 (SNRs) 中表现出卓越的解码性能,突出了稳定性.

    结论:

    • 拟议的无监督特征提取技术有效地减少了数据集漂移,并增强了基于EMG的电机意图解码.
    • 该方法在准确性和稳定性方面提供了显著的改进,优于传统方法.
    • 这种技术有望在现实应用中提高EMG控制系统的可靠性.