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Customized Access Technology for Children using Head Movement Recognition.

Silvia Orlandi, Fanny Hotze, Derrick Lim

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study developed an accurate head movement recognition algorithm for children with complex communication needs. This technology offers a promising alternative access method for individuals with limited mobility.

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    Area of Science:

    • Assistive Technology
    • Human-Computer Interaction
    • Biomedical Engineering

    Background:

    • Children with cerebral palsy and complex communication needs have limited access to technology (AT).
    • Existing ATs like speech recognition and mechanical switches are often insufficient for individuals with speech and motor impairments.
    • Head movement recognition presents a potential AT solution for this population.

    Purpose of the Study:

    • To implement and evaluate a head movement recognition algorithm for children.
    • To assess the feasibility of using head movements as an input method for AT.
    • To adapt existing head pose estimation techniques for pediatric use.

    Main Methods:

    • Developed a head movement recognition algorithm using face-tracking and landmark detection.
    • Employed the Pose from Orthography and Scaling with Iterations (POSIT) algorithm for head pose estimation.
    • Utilized Hidden Markov Models (HMMs) to classify three distinct head movements.
    • Evaluated the algorithm on videos of children playing a videogame in a naturalistic setting.

    Main Results:

    • The algorithm achieved up to 95.6% accuracy in predicting head movements in typically developing children.
    • Demonstrated the robustness of head pose estimation algorithms in a pediatric context.
    • Successfully classified head movements during a videogame task.

    Conclusions:

    • Head movement recognition is a viable and accurate AT method for children.
    • This technology can significantly enhance communication and technology access for children with complex needs.
    • Further research should focus on validating these findings in children with cerebral palsy and other disabilities.