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

Force Classification01:22

Force Classification

2.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.8K

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

Scoping Review of Machine Learning Techniques in Marker-Based Clinical Gait Analysis.

Bioengineering (Basel, Switzerland)·2025
Same author

A Pilot Dashboard System to Track Cumulative Exposure to Sound Levels during Music Instruction: A Technical Report.

Journal of the American Academy of Audiology·2024
Same author

Machine Learning Models for Parkinson Disease: Systematic Review.

JMIR medical informatics·2024
Same author

Creating an autoencoder single summary metric to assess gait quality to compare surgical outcomes in children with cerebral palsy: The Shriners Gait Index (SGI).

Journal of biomechanics·2024
Same author

AI-organoid integrated systems for biomedical studies and applications.

Bioengineering & translational medicine·2024
Same author

Feasibility of PROMIS using computerized adaptive testing during inpatient rehabilitation.

Journal of patient-reported outcomes·2023

相关实验视频

Updated: May 6, 2026

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

一个基于LSTM的手势到语音识别系统.

Riyad Bin Rafiq1, Syed Araib Karim1, Mark V Albert2

  • 1Department of Computer Science and Engineering, University of North Texas, Denton, Texas, USA.

Proceedings. IEEE International Conference on Healthcare Informatics
|February 26, 2024
PubMed
概括

这项研究开发了一个使用手势手势的移动应用程序,用于语言障碍者. 该系统在识别受过训练的手动时达到91.8%的准确性,增强了无法说话的人的沟通能力.

科学领域:

  • 辅助技术 辅助技术 辅助技术
  • 人与计算机的交互
  • 生物医学工程 生物医学工程

背景情况:

  • 有限的沟通选择阻碍了言语障碍患者的社交互动.
  • 传统的方法,如打字,对于那些有精细运动技能缺陷的人来说可能具有挑战性.
  • 基于手势的沟通为增强社会动态提供了一个潜在的替代方案.

研究的目的:

  • 开发一个移动应用程序原型,用于从手势中生成可听响应.
  • 使用加速度计数据创建一个快速,定制的手势识别模型训练系统.
  • 改善言语和运动障碍者沟通的可访问性.

主要方法:

  • 开发了一个集成双向长短期内存 (LSTM) 网络的移动应用程序.
  • 收集了来自六名参与者执行11种不同的手势的加速度计数据.
  • 训练LSTM模型使用收集的手势数据进行识别.
  • 使用嵌套主体智能交叉验证评估模型性能.

主要成果:

  • 双向LSTM模型在识别11个预先选择的手势时实现了91.8%的总体回忆.
  • 当两种常被混的手势被排除在评估之外时,回忆能力提高到95.8%.
关键词:
加速度计的加速计是什么?双向的LSTM是一个双向的LSTM.这是手势识别,是手势识别.移动应用程序移动应用程序语言障碍 语言障碍 语言障碍

更多相关视频

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K
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

484

相关实验视频

Last Updated: May 6, 2026

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
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K
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

484
  • 展示了个性化手势识别移动系统的可行性.

    • 结论:

    • 这种原型代表了为语音障碍者提供移动通信系统的重要一步.
    • 进一步的改进可以增强手势识别和模型定制以满足个体需求.
    • 该系统有可能显著提高社交互动和沟通效率.