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A sign-component-based framework for Chinese sign language recognition using accelerometer and sEMG data.

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

IEEE Transactions on Bio-Medical Engineering

|March 23, 2012

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This study introduces a novel framework for automatic Chinese sign language recognition (SLR) using accelerometer (ACC) and surface electromyographic (sEMG) sensors. The component-level approach significantly improves recognition accuracy for large vocabularies.

Area of Science:

Computer Science
Biomedical Engineering
Artificial Intelligence

Background:

Sign language recognition (SLR) systems benefit from identifying individual gesture components, particularly for large vocabularies.
Existing SLR methods often struggle with the complexity and nuances of large-scale sign language interpretation.
Portable sensors like accelerometers (ACC) and surface electromyography (sEMG) offer potential for unobtrusive and effective data capture.

Purpose of the Study:

To develop an automatic Chinese sign language recognition framework operating at the component level.
To leverage ACC and sEMG sensor data for modeling fundamental sign components: hand shape, orientation, and movement.
To enhance SLR performance through a novel decision-level fusion of component likelihoods.

Main Methods:

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A framework for automatic Chinese sign language recognition (SLR) was developed using accelerometer (ACC) and surface electromyographic (sEMG) sensors.
Continuous sign language sentences were segmented into subword units for component-level analysis.
Features extracted from ACC and sEMG data were used to model hand shape, orientation, and movement, with component classifiers learned.
A decision-level fusion strategy combined component likelihoods for subword sequence recognition.

Main Results:

The proposed component-level SLR framework achieved an overall classification accuracy of 96.5% for a vocabulary of 120 signs.
Recognition accuracy for 200 sentences reached 86.7%, demonstrating the feasibility of interpreting sign components from sensor data.
The method showed superior performance compared to previous subword-level SLR approaches.

Conclusions:

Interpreting sign components from ACC and sEMG data is feasible and highly effective for SLR.
The proposed component-level framework offers a promising approach for implementing large-vocabulary, portable sign language recognition systems.
This research advances the development of more accurate and accessible SLR technologies.