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 journal

Correction: Ma et al. A Lightweight, Low-Frequency, Broadband Underwater Acoustic Transducer with Ternary Symmetric Excitation: Integrating KNN and Terfenol-D for Enhanced Performance. <i>2026</i>, <i>26</i>, 3645.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: He et al. An Edge-Computing-Based Emotion-Aware Adaptive Lighting System for Intelligent Cockpits. <i>Sensors</i> 2026, <i>26</i>, 3489.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: Tu et al. Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography. <i>Sensors</i> 2024, <i>24</i>, 3097.

Sensors (Basel, Switzerland)·2026
Same journal

Real-Time Detection System for Road Roughness Based on Ultrasonic Technology.

Sensors (Basel, Switzerland)·2026
Same journal

FedHSFV: Federated Learning for Finger Vein Recognition via Hierarchical Decoupling and Subspace Metric.

Sensors (Basel, Switzerland)·2026
Same journal

Vibration-Based Machine Learning Model Training for Railway Bridge Health Monitoring.

Sensors (Basel, Switzerland)·2026

相关实验视频

Updated: Jul 20, 2025

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

智能手机身份验证系统使用个人步态和深度学习模型.

Jiwoo Choi1, Sangil Choi1, Taewon Kang1

  • 1Department of Computer Science and Engineering, Gangneung-Wonju National University, Wonju 26403, Republic of Korea.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括

这项研究引入了使用人类步态的新型智能手机认证系统,达到90%以上的准确性. 这种步态分析方法显著减少了数据收集时间,以可靠地识别用户.

科学领域:

  • 计算机科学 计算机科学
  • 生物识别信息 生物识别信息
  • 人与计算机的交互

背景情况:

  • 在超连接的社会中,安全的信息共享至关重要.
  • 传统的智能手机身份验证方法需要改进以提高安全性.
  • 识别未经授权的用户对于保护敏感数据至关重要.

研究的目的:

  • 提出一种新的智能手机身份验证系统,利用人类的步态.
  • 开发用于步态特征提取的深度学习模型.
  • 验证系统的可靠性和效率,以验证用户身份.

主要方法:

  • 使用卷积神经网络 (CNN) 深度学习模型来学习人类步行特征.
  • 经过训练的模型被集成到智能手机中进行实时身份验证.
  • 用户通过携带智能手机在手中行走1.8秒来验证身份.

主要成果:

  • 拟议的步态认证系统实现了平均准确度,精确度,回忆率和F1分数至少为90%.
  • 步行数据收集时间从7秒缩短到1.8秒,增长了四倍.
  • 该系统在识别合法用户方面表现出高度可靠性.
关键词:
认证的真实性 认证的真实性卷积神经网络是一种卷积神经网络.人类的步态是人类的步态机器学习是机器学习.

更多相关视频

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K
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

639

相关实验视频

Last Updated: Jul 20, 2025

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
Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K
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

639

结论:

  • 人类步态可以有效地被用作智能手机的新生物识别身份验证方法.
  • 开发的系统为传统的身份验证技术提供了安全有效的替代方案.
  • 短时间的步行数据足以进行强大的用户身份验证,并显示出显著的性能改进.