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相关实验视频

Updated: Jan 12, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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增强手势识别,以帮助视障人士使用基于物联网环境的改进蛇优化算法深度学习来帮助视障人士.

Hanan Abdullah Mengash1, Basma S Alqadi2, Radwa Marzouk3,4

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia. hamengash@pnu.edu.sa.

Scientific reports
|November 1, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了使用深度学习的先进手势识别系统和改进的蛇优化算法,以帮助视力障碍者. 这种新的方法实现了98.62%的准确性,大大改善了实时手势解释.

关键词:
深度学习是一种深度学习.在手势识别,手势识别.物联网的物联网,就是物联网.蛇优化算法 蛇优化算法视力受损的人视力受损的人.

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相关实验视频

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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:

  • 手势识别 (GR) 对接口至关重要,但对视力受损用户来说却面临着挑战.
  • 传统的机器学习 (ML) 难以实现实时性能,因此需要先进的解决方案.
  • 深度学习 (DL) 在GR中为复杂模式识别提供了卓越的能力.

研究的目的:

  • 在物联网环境中为视力受损者开发一个增强的手势识别系统 (EGRVI-DLISOA).
  • 利用深度学习和改进的蛇优化算法来实现准确的实时手势解释.
  • 解决视力障碍者在日常任务和技术交互中所面临的挑战.

主要方法:

  • 使用Sobel过器 (SF) 来消除手势数据中的噪音.
  • 使用SqueezeNet模型从视觉数据中高效地提取特征.
  • 实现了用于手势分类的长短期记忆 (LSTM),通过改进的蛇优化算法 (ISOA) 进行了优化.

主要成果:

  • 在手势识别方面,EGRVI-DLISOA技术表现出了卓越的性能.
  • 在手势数据集上实现了98.62%的高准确率.
  • 在实验评估中表现优于现有的手势识别模型.

结论:

  • EGRVI-DLISOA方法显著提高了视力受损用户的手势识别.
  • 深度学习与优化算法相结合,为实时辅助技术提供了强大的解决方案.
  • 该系统为视力障碍者提供了计算机界面的有希望的进步.