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Analysis and Usage: Subject-to-subject Linear Domain Adaptation in sEMG Classification.

Takayuki Hoshino, Suguru Kanoga, Masashi Tsubaki

    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
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    Summary
    This summary is machine-generated.

    Linear domain adaptation for biosignal applications requires user-specific calibration. This study reveals that source-target data correlation significantly impacts classification accuracy, suggesting non-linear approaches for low-correlation scenarios.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Signal Processing

    Background:

    • Biosignal applications necessitate lengthy calibration to adapt pre-trained classifiers to new user data.
    • Linear domain adaptation (DA) transfer learning methods are explored to reduce calibration time by transferring pooled source data to target data.
    • Previous applications of linear DA to surface electromyogram (sEMG) data assumed linearity, which contradicts the typically non-linear nature of sEMG.

    Purpose of the Study:

    • To investigate the impact of source-target data correlation on 8-class forearm movement classification using linear DA approaches.
    • To determine the conditions under which linear DA may lead to negative transfer due to non-linear characteristics of sEMG data.

    Main Methods:

    • Applied linear domain adaptation (DA) transfer learning techniques to surface electromyogram (sEMG) datasets.
    • Analyzed the correlation between source and target sEMG data across different forearm movement classes.
    • Evaluated the classification accuracy of an 8-class forearm movement classifier based on varying source-target correlations.

    Main Results:

    • A significant positive correlation was observed between classification accuracy and the source-target data correlation.
    • The degree of source-target correlation was found to be dependent on the specific motion class being analyzed.
    • Linear DA approaches demonstrated reduced effectiveness when the source-target correlation was low, indicating potential negative transfer.

    Conclusions:

    • The correlation between source and target data is a critical factor influencing the performance of linear DA in sEMG classification.
    • Forearm movement classification accuracy is positively associated with higher source-target data correlation.
    • Non-linear DA approaches are recommended when source-target correlation is low, particularly across different subjects or motion classes, to avoid negative transfer.