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An LSTM-based Gesture-to-Speech Recognition System.

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
Summary
This summary is machine-generated.

This study developed a mobile app using hand gestures for speech-impaired individuals. The system achieved 91.8% accuracy in recognizing trained hand movements, enhancing communication for those unable to speak.

Keywords:
accelerometerbidirectional LSTMgesture recognitionmobile applicationspeech impairment

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Area of Science:

  • Assistive Technology
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Limited communication options hinder social interaction for individuals with speech impairments.
  • Traditional methods like typing may be challenging for those with fine motor skill deficits.
  • Gesture-based communication offers a potential alternative for enhanced social dynamics.

Purpose of the Study:

  • To develop a mobile application prototype for generating audible responses from hand gestures.
  • To create a system for rapid, tailored gesture recognition model training using accelerometer data.
  • To improve communication accessibility for individuals with speech and motor impairments.

Main Methods:

  • Developed a mobile application integrating a bidirectional Long Short-Term Memory (LSTM) network.
  • Collected accelerometer data from six participants performing 11 distinct hand gestures.
  • Trained the LSTM model using the collected gesture data for recognition.
  • Evaluated model performance using nested subject-wise cross-validation.

Main Results:

  • The bidirectional LSTM model achieved an overall recall of 91.8% in recognizing 11 pre-selected hand gestures.
  • Recall improved to 95.8% when two commonly confused gestures were excluded from assessment.
  • Demonstrated the feasibility of a mobile system for personalized gesture recognition.

Conclusions:

  • The prototype represents a significant step towards a mobile communication system for speech-impaired populations.
  • Further refinement can enhance gesture recognition and model tailoring for individual needs.
  • The system has the potential to significantly improve social interaction and communication efficiency.