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Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review.

Karly Kudrinko, Emile Flavin, Xiaodan Zhu

    IEEE Reviews in Biomedical Engineering
    |August 27, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This review analyzes wearable sensor systems for sign language recognition, identifying trends and challenges in gesture classification. Findings can help develop better assistive technologies for the Deaf and hard of hearing community.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Assistive Technology

    Background:

    • Sign language is crucial for communication for the Deaf, deafened, hard of hearing, and non-verbal populations.
    • Communication barriers persist for these individuals interacting with non-signers.
    • Technological advancements, particularly in machine learning, offer new solutions for gesture recognition.

    Purpose of the Study:

    • To review and analyze studies on wearable sensor-based systems for sign language gesture classification.
    • To identify trends, best practices, and challenges in this research area.
    • To inform the development of user-centered and robust sign language recognition systems.

    Main Methods:

    • A literature review of 72 studies published between 1991 and 2019.
    • Analysis of attributes including sign language variation, sensor configuration, classification methods, study design, and performance metrics.
    • Comparative analysis to identify commonalities and differences across studies.

    Main Results:

    • Identified key trends in the evolution of wearable sensor technology for sign language recognition.
    • Highlighted common challenges such as sensor placement, data variability, and generalization.
    • Compared various classification algorithms and their effectiveness.

    Conclusions:

    • Wearable sensor-based systems show significant potential for sign language recognition.
    • Further research is needed to address current limitations and improve system robustness and user-friendliness.
    • This review provides a foundation for future development of effective sign language recognition technologies.