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Interpreting sign components from accelerometer and sEMG data for automatic sign language recognition.

Yun Li1, Xiang Chen, Xu Zhang

  • 1Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. liyun5@mail.ustc.edu.cn

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
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Summary

This study introduces a novel method for sign language recognition (SLR) using accelerometer (ACC) and surface electromyographic (sEMG) sensors. The component-level approach effectively interprets sign components, advancing portable SLR systems.

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Sign language recognition (SLR) systems are crucial for communication accessibility.
  • Large-vocabulary SLR systems require efficient methods for gesture component identification.
  • Existing sensor-based SLR methods can be limited in scope and portability.

Purpose of the Study:

  • To propose and evaluate an automatic, component-level sign language recognition method.
  • To utilize portable accelerometer (ACC) and surface electromyographic (sEMG) sensors for SLR.
  • To enhance the performance and feasibility of large-vocabulary portable SLR systems.

Main Methods:

  • Developing a novel method for automatic SLR at the component level.
  • Integrating data from accelerometer (ACC) and surface electromyographic (sEMG) sensors.
  • Analyzing the constituent components of sign gestures for recognition.

Main Results:

  • Demonstrated the effectiveness of the proposed component-level SLR method.
  • Showcased the feasibility of interpreting sign components from ACC and sEMG data.
  • Achieved improved performance in SLR using ACC and sEMG sensors.

Conclusions:

  • The component-level interpretation of sign gestures from ACC and sEMG data is feasible.
  • The proposed method enhances SLR performance, paving the way for large-vocabulary portable systems.
  • This research promotes the development of more accessible and practical sign language recognition technologies.