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

An End-to-End Deep Learning System for Gastrointestinal Bleeding Detection and Quantification in Wireless Capsule Endoscopy.

Diagnostics (Basel, Switzerland)·2026
Same author

Multivariable Signal Processing for Characterization of Failure Modes in Thin-Ply Hybrid Laminates Using Acoustic Emission Sensors.

Sensors (Basel, Switzerland)·2023
Same author

Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms.

Diagnostics (Basel, Switzerland)·2022
Same author

Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms.

Diagnostics (Basel, Switzerland)·2022
Same author

Identification of Autism in Children Using Static Facial Features and Deep Neural Networks.

Brain sciences·2022
Same author

A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT).

Journal of autism and developmental disorders·2021

相关实验视频

Updated: Jun 14, 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

414

通过表面电肌图和机器学习增强手势识别.

Mujeeb Rahman Kanhira Kadavath1, Mohamed Nasor1, Ahmed Imran1

  • 1College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括

这项研究使用表面电肌图 (EMG) 信号和机器学习来解码手势. 随机森林模型达到99%以上的准确性,显示了其在康复和人机交互方面的潜力.

科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 表面电肌图 (EMG) 信号提供了一种非侵入性的方法来捕捉神经肌肉活动.
  • 精确的解码手势对于先进的假肢和人机接口至关重要.
  • 机器学习算法可以有效地解释复杂的EMG模式.

研究的目的:

  • 评估机器学习模型在从EMG数据中分类手势时的有效性.
  • 为了确定实时手势识别的最佳机器学习模型.
  • 探索在医疗保健和人机交互方面的潜在应用.

主要方法:

  • 使用Myo-armband传感器获取EMG信号,用于七种不同的手势.
  • 数据经过预处理,用于特征提取和标记.
  • 通过交叉验证,训练,优化和评估了四种传统的机器学习模型.

主要成果:

  • 随机森林模型在分类手势方面表现出卓越的表现.
  • 在所有手势课程中,精度,回忆和F1分数始终很高.
  • 在随机森林模型中,接收器运行特征曲线下面的区域 (ROC-AUC) 得分超过99%.
关键词:
的 AUC-ROC 值.在EMGEMGEMGEMGEMGEMGEMGEMGEM这是一个EMG传感器.在我的手臂上戴着Myo腕带.进行交叉验证.一个电心图 (electromyogram) 是一个电心图.手的手势手势手势机器学习是机器学习.随机的森林随机的森林

更多相关视频

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.5K
Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction
09:14

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Published on: September 28, 2019

11.4K

相关实验视频

Last Updated: Jun 14, 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

414
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.5K
Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction
09:14

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Published on: September 28, 2019

11.4K

结论:

  • 随机森林模型非常有效地从EMG数据中对手势进行分类.
  • 这种方法对改善医疗保健康复工程具有重大前景.
  • 这些发现表明,通过精确的基于EMG的手势识别,人类与计算机交互技术的进步.