一个基于手势识别的信号系统,用于使用机器学习的人
Rashmi Dagde1, Swapnil Thakre1, Sonam Chopade1
1Ramdeobaba University, Shri Ramdeobaba College of Engineering and Management Nagpur.
MethodsX
|November 13, 2025
概括
这项研究介绍了一种轻量级的手势识别系统,用于改善的人的沟通. 该框架在实时实现了94.1%的准确性,为辅助通信应用提供了可访问的解决方案.
科学领域:
- 计算机科学 计算机科学
- 人与计算机的交互
- 辅助技术 辅助技术 辅助技术
背景情况:
- 有效的沟通至关重要,但言语障碍患者面临重大障碍.
- 现有的手语识别系统通常是计算密集型的,限制了在适度硬件上的现实应用.
- 一般公众缺乏手语能力加剧了社区的沟通挑战.
研究的目的:
- 开发一个轻量级和高效的手势识别框架,用于实时通信.
- 通过使用可访问技术,使言语障碍患者能够进行直观的沟通.
- 为了弥合的人与一般公众之间的沟通差距.
主要方法:
- 整合MediaPipe手指地标检测与静态和动态手势识别的双分类器.
- 开发一个适合在低资源硬件上部署的轻量级框架.
- 使用3500个样本的均衡数据集进行测试,测试七种代表性手势 (A,B,C,D,打开,关闭,OK).
主要成果:
- 在一个分区的测试套件上获得了94.1%的准确性.
- 仅在CPU部署时,以每秒30 (FPS) 的持续实时性能.
- 在各种照明条件下表现出强性.
结论:
- 拟议的框架为辅助通信提供了高性能准确性,效率和可访问性的平衡.
- 这种方法提供了一个可行的,低资源的实时手势识别方法.
- 该系统提高了言语障碍患者的沟通可访问性.
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