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

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role of...

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

PlantEFRSegnet: A Plant Point Cloud Segmentation Network Based on Edge Point Preservation and Feature Feedback Repair.

Sensors (Basel, Switzerland)·2026
Same author

ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection.

Sensors (Basel, Switzerland)·2025
Same author

ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution.

Sensors (Basel, Switzerland)·2023
Same author

A Local and Non-Local Features Based Feedback Network on Super-Resolution.

Sensors (Basel, Switzerland)·2022
Same author

MASPC_Transform: A Plant Point Cloud Segmentation Network Based on Multi-Head Attention Separation and Position Code.

Sensors (Basel, Switzerland)·2022
Same author

A Single Stage and Single View 3D Point Cloud Reconstruction Network Based on DetNet.

Sensors (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: May 13, 2026

Methods to Test Visual Attention Online
09:44

Methods to Test Visual Attention Online

Published on: February 19, 2015

11.9K

基于面部检测和头部姿势估计的在线学习状态评估方法

Bin Li1, Peng Liu1

  • 1School of Computer Science, Northeast Electric Power University, Jilin 132011, China.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种轻量级的学习状态评估方法,使用面部检测和头部姿势估计. 该系统有效地评估学生在移动设备上的注意力,而不妨碍学习过程.

关键词:
面部检测 面部检测 面部检测估计头部姿势的估计.在线学习状态评估在线学习状态评估

更多相关视频

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

5.3K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

相关实验视频

Last Updated: May 13, 2026

Methods to Test Visual Attention Online
09:44

Methods to Test Visual Attention Online

Published on: February 19, 2015

11.9K
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

5.3K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 人与计算机的交互

背景情况:

  • 在线学习需要有效的方法来监测学生的参与度和注意力.
  • 评估学习状态的现有方法往往需要大量的计算资源,这限制了它们在移动设备上的使用.
  • 需要高效的算法,能够准确地实时评估学生的学习状态.

研究的目的:

  • 为移动设备提出一个计算效率高的学习状态评估方法.
  • 开发用于面部检测和头部姿势估计的轻量级网络.
  • 创建一个算法,根据面部和头部姿势数据来评估学生的注意力.

主要方法:

  • 开发了一个基于幽灵和注意模块 (GA) 的面部检测网络 (GA-Face),利用幽灵模块和无参数的注意力机制来减少计算负载.
  • 为了高效的头部姿势分析,提出了一个轻量级的双分支 (DB) 头部姿势估计网络 (DB-Net).
  • 设计了一个学生学习状态评估算法,集成面对屏幕距离和头部姿势分析.

主要成果:

  • GA-Face和DB-Net在标准面部检测和头部姿势估计数据集上表现出有效性.
  • 拟议的方法通过实践案例得到验证,显示了对学生注意力和集中力的准确评估.
  • 该方法的低计算复杂性确保它不会干扰学生的学习过程.

结论:

  • 提出的学习状态评估方法是有效的,适用于资源有限的移动设备.
  • 在学习评估中,GA-Face和DB-Net提供了面部检测和头部姿势估计的高效解决方案.
  • 这种方法提供了一种非侵入性的方法来监测学生在在线学习环境中的参与.