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 association between epicardial adipose tissue and prognosis in acute myocardial infarction: a systematic review and meta-analysis.

Journal of thoracic disease·2026
Same author

A bibliometric analysis of global research trends and emerging hotspots on suicide and depression among children and adolescents.

Discover mental health·2026
Same author

Comparison of the modified Broström-Gould procedure for chronic lateral ankle instability in adolescents with and without an os subfibulare : a propensity score-matched study.

The bone & joint journal·2026
Same author

Shear Behavior of GMTC/BPC-GCL Interface Under Dry and Hydrated Conditions with Varying Polymer Content.

Polymers·2026
Same author

Occupational and psychosocial correlates of sleep disturbance among Chinese expatriate employees in Iraq's Maysan oilfields: a cross-sectional study using regression and network analysis.

Frontiers in psychiatry·2026
Same author

From CAT-like to POD-like enzymatic activity of Cu-BHT tuning by substrate engineering.

Physical chemistry chemical physics : PCCP·2026

相关实验视频

Updated: Sep 14, 2025

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.5K

基于GMM-HMM的眼动分类,用于高效和直观的动态人机交互系统.

Jiacheng Xie1, Rongfeng Chen1, Ziming Liu1

  • 1Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230027, China; jc_xie@mail.ustc.edu.cn (J.X.); crf0114@mail.ustc.edu.cn (R.C.); lzm1224@mail.ustc.edu.cn (Z.L.); jh152728@mail.ustc.edu.cn (J.Z.).

Journal of eye movement research
|July 25, 2025
PubMed
概括

这项研究引入了一种新的算法,用于在辅助机器人手臂 (ARA) 中实时分类眼动. 该GMM-HMM模型提高了人机交互的准确性和速度.

关键词:
在GMM-HMM之间.有助于机器人手臂的机器人臂眼睛的运动 眼睛的运动人与计算机的互动.

更多相关视频

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
07:26

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

8.0K
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

746

相关实验视频

Last Updated: Sep 14, 2025

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.5K
Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
07:26

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

8.0K
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

746

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人与计算机的交互
  • 生物医学工程 生物医学工程

背景情况:

  • 眼睛跟踪技术增强了人工机器人互动 (HCI) 的辅助机器人手臂 (ARA).
  • 目前依赖视线的方法存在"Midas Touch"问题,限制了动态控制.
  • 实时眼动分类对于高效准确的人机交互至关重要.

研究的目的:

  • 提出一个新的高斯混合模型-隐藏马尔科夫模型 (GMM-HMM) 分类算法.
  • 在动态的人机交互中克服传统方法的局限性.
  • 提高辅助机器人系统的直观性和效率.

主要方法:

  • 开发了一个GMM-HMM分类算法,用于实时分析眼动.
  • 包含基于二次误差 (SSE) 的特征提取和层次训练的总和.
  • 将算法与辅助机器人臂集成,用于基于视线轨迹的路径规划.

主要成果:

  • 获得了94.39%的分类准确率,显著超过现有方法.
  • 将平均路径规划时间缩短到2.97毫秒.
  • 在动态的人机交互场景中展示了有效和直观的控制.

结论:

  • 拟议的GMM-HMM算法为动态的人机交互提供了一个高效和直观的解决方案.
  • 这种方法提高了辅助机器人系统的性能.
  • 为复杂的现实应用提供了HCI未来进步的坚实框架.