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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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基于智能数据手套的手势识别用于两通信.

Liufeng Fan1, Zhan Zhang1, Biao Zhu2

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.

Micromachines
|November 25, 2023
PubMed
概括

一个带有灵活传感器和IMU的智能数据手套可以识别25种静态和10种动态的手势. 适应性模型可确保在各种环境中进行两通信的高精度.

关键词:
两通信是两通信.深度学习是一种深度学习.手的手势识别手势识别智能数据手套智能数据手套转移学习转移学习水下手势识别系统是水下手势识别系统.

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科学领域:

  • 人与计算机的交互
  • 可穿戴技术可穿戴技术
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 在各种环境 (包括陆地和水下) 中进行有效的沟通面临重大挑战.
  • 现有的手势识别系统经常与环境适应性和用户可变性作斗争.

研究的目的:

  • 开发一个智能数据手套系统,用于识别一套全面的静态和动态手势.
  • 引入一种能够适应不同环境的新型两等级手势识别 (AHGR) 模型.
  • 为了实现两通信应用的高精度手势识别.

主要方法:

  • 制造智能数据手套,集成五通道灵活电容拉伸传感器和六轴惯性测量单元 (IMU).
  • 开发一种两等级手势识别 (AHGR) 模型,在复杂的 (SqueezeNet-BiLSTM) 和轻量级 (SVD优化的光谱聚类) 算法之间进行自适应切换.
  • 实现基于域区分网络 (DSN) 的转移学习模型,以实现用户和设备独立性.

主要成果:

  • 该SqueezeNet-BiLSTM模型在陆地环境中实现了98.21%的动态手势识别准确度.
  • 该SVD优化的光谱聚类模型在水下手势识别方面实现了98.35%的准确性.
  • 基于DSN的传输模型确保了新用户和手套设备的识别准确率为94%.

结论:

  • 开发的智能数据手套和AHGR模型为两体手势通信提供了强大的解决方案.
  • 适应式手势识别模型在不同的环境条件下显著提高了准确性和有效性.
  • 该系统展示了在各种环境中无的人机交互和通信的潜力.