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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication.

Liufeng Fan1, Zhan Zhang1, Biao Zhu2

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

Micromachines
|November 25, 2023
PubMed
Summary
This summary is machine-generated.

A smart data glove with flexible sensors and IMU recognizes 25 static and 10 dynamic hand gestures. An adaptive model ensures high accuracy for amphibious communication in diverse environments.

Keywords:
amphibious communicationdeep learninghand gesture recognitionsmart data glovetransfer learningunderwater gesture recognition

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Area of Science:

  • Human-Computer Interaction
  • Wearable Technology
  • Robotics

Background:

  • Effective communication in diverse environments, including land and underwater, presents significant challenges.
  • Existing gesture recognition systems often struggle with environmental adaptability and user variability.

Purpose of the Study:

  • To develop a smart data glove system for recognizing a comprehensive set of static and dynamic hand gestures.
  • To introduce a novel amphibious hierarchical gesture recognition (AHGR) model capable of adapting to different environments.
  • To achieve high-accuracy gesture recognition for amphibious communication applications.

Main Methods:

  • Fabrication of a smart data glove integrating five-channel flexible capacitive stretch sensors and a six-axis inertial measurement unit (IMU).
  • Development of an amphibious hierarchical gesture recognition (AHGR) model with adaptive switching between complex (SqueezeNet-BiLSTM) and lightweight (SVD-optimized spectral clustering) algorithms.
  • Implementation of a domain separation network (DSN)-based transfer learning model for user and device independence.

Main Results:

  • The SqueezeNet-BiLSTM model achieved 98.21% accuracy for dynamic gesture recognition in land environments.
  • The SVD-optimized spectral clustering model achieved 98.35% accuracy for underwater gesture recognition.
  • The DSN-based transfer model ensured 94% recognition accuracy for new users and glove devices.

Conclusions:

  • The developed smart data glove and AHGR model offer a robust solution for amphibious gesture communication.
  • Adaptive gesture recognition models significantly enhance accuracy and effectiveness across different environmental conditions.
  • The system demonstrates potential for seamless human-robot interaction and communication in varied settings.