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Mordo2: A Personalization Framework for Silent Command Recognition.

Chunzhi Yi, Baichun Wei, Jianfei Zhu

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    This study introduces a personalization framework for silent command recognition using around-ear biosensors. The new method significantly improves accuracy and reduces data needs for new users, aiding wearable technology development.

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    Area of Science:

    • Human-computer interaction
    • Wearable technology
    • Biosignal processing

    Background:

    • Intelligent wearables necessitate intuitive micro-interactions, posing challenges for social integration.
    • Previous research showed silent command recognition via around-ear biosensors, but user adaptation remained a limitation.

    Purpose of the Study:

    • To develop a personalization framework to overcome user adaptation limitations in silent command recognition.
    • To enhance the accuracy and efficiency of wearable human-computer interactions.

    Main Methods:

    • Integrated spectral factorization of signals, temporal confidence rejection, and transfer learning.
    • Proposed supervised spectral factorization to improve signal-to-noise ratio by mapping around-ear and facial muscle signals.
    • Introduced time decay into confidence rejection to leverage command continuity.

    Main Results:

    • Achieved an average accuracy of 92.38%, significantly outperforming methods using only transfer learning.
    • Demonstrated comparable accuracy with substantially reduced data for new users.
    • Validated significant improvements in user adaptation accuracy.

    Conclusions:

    • The developed framework effectively addresses user adaptation challenges in silent command recognition.
    • This advancement facilitates commercialization of silent command recognition and inspires solutions for wearable HCI adaptation.
    • The study paves the way for more seamless and personalized wearable human-computer interactions.