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

Learned spatially varying microscopy model with adaptive point spread functions.

Optics express·2026
Same author

Transformer-Based Context-Informed Incremental Learning With sDTW Alignment Unlocks Fast and Precise Regression-Based Myoelectric Control.

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

Readout Techniques and Offset Compensation Strategies for Biomedical Resistive MEMS Sensors: A Comprehensive Review.

IEEE reviews in biomedical engineering·2025
Same author

Fully-Flexible Multifunctional Polydimethylsiloxane (PDMS) Neural Probe With a U-Turn Polyester Microchannel.

IEEE transactions on bio-medical engineering·2025
Same author

Context Informed Incremental Learning Improves Myoelectric Control Performance in Virtual Reality Object Manipulation Tasks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

The Fifth Main Dynamic Factor: Skin Temperature and Its Effects on EMG-Based Gesture Recognition.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

相关实验视频

Updated: May 24, 2025

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

361

视觉场景理解用于增强的EMG手势识别.

Felix Chamberland, Thomas Labbe, Simon Tam

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括

    这项研究结合了电肌图 (EMG) 和计算机视觉 (CV) 以实现可靠的实时手势识别在假肢控制中. 多式联网系统通过将视觉上下文与EMG信号集成,提高了控制稳定性和用户指挥.

    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
    • 人与计算机的交互

    背景情况:

    • 肌电假肢依靠电肌图 (EMG) 进行控制,但容易受到噪音和错误激活的影响.
    • 现实世界的应用需要强大的手势识别,考虑到环境背景.
    • 整合多种传感器模式可以提高假肢控制系统的可靠性和安全性.

    研究的目的:

    • 开发和评估一个多式联络框架,用于在肌电假肢控制中增强实时手势识别.
    • 用计算机视觉 (CV) 来增强基于EMG的手势识别,以实现上下文意识.
    • 通过防止错误的手势检测来减轻假肢控制中的错误运动.

    主要方法:

    • 实施了一种多模式的方法,结合了电肌学 (EMG) 和计算机视觉 (CV).
    • 一个语深卷积神经网络 (SDCNN) 用于EMG手势识别.
    • 一个定制的YOLO计算机视觉模型被用于对象检测,以提供上下文信息.
    • 传感器融合将SDCNN预测与YOLO模型的背景集成在一起.

    主要成果:

    • 多式联网系统在现实环境中展示了强大的实时手势识别.
    • 来自CV的上下文信息有效地减轻了在发病和维持期间的虚假手势检测.

    更多相关视频

    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
    Extraction of the EPP Component from the Surface EMG
    07:16

    Extraction of the EPP Component from the Surface EMG

    Published on: December 16, 2009

    12.5K

    相关实验视频

    Last Updated: May 24, 2025

    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

    361
    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
    Extraction of the EPP Component from the Surface EMG
    07:16

    Extraction of the EPP Component from the Surface EMG

    Published on: December 16, 2009

    12.5K
  • 试点实验证实了手势控制界面的增强强性.
  • 由于控制精度的提高,用户可以更好地控制假肢系统.

    • 结论:

    • 多式传感器融合EMG和CV为可靠的肌电假肢控制提供了一个有希望的方法.
    • 情境感知框架显著提高了先进假肢设备的安全性和可用性.
    • 这种集成系统通过增加界面的稳定性和用户控制来增强人类的意志控制.