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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Time-domain Mixup Source Data Augmentation of sEMGs for Motion Recognition towards Efficient Style Transfer Mapping.

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Data augmentation using mixup enhances transfer learning (TL) for surface electromyogram (sEMG) motion recognition, improving classifier accuracy despite limited source data. This technique addresses overfitting in TL models for wearable sensing applications.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Wearable Technology

    Background:

    • Surface electromyogram (sEMG) offers promising applications for motion recognition due to its integration with wearable devices and high signal-to-noise ratio.
    • Inter-subject variability and limited data availability pose significant challenges for developing accurate sEMG-based motion recognition classifiers.
    • Transfer learning (TL) can mitigate inter-subject variability, but small source datasets risk overfitting in TL-combined classifiers.

    Purpose of the Study:

    • To evaluate the impact of the mixup data augmentation technique on the accuracy of motion recognition using TL.
    • To assess the performance of TL-combined classifiers with augmented versus non-augmented datasets.
    • To investigate the effectiveness of mixup in mitigating overfitting in TL models with limited source data.

    Main Methods:

    • Utilized an 8-class sEMG dataset from 25 subjects, recorded using wearable sensors.
    • Employed a time-domain data augmentation method, mixup, to increase the source dataset size up to 10 times.
    • Compared the performance of motion recognition classifiers (support vector machine and a 4-layered fully connected feedforward neural network) with and without TL, and with augmented data.

    Main Results:

    • The mixup data augmentation method significantly improved the performance of TL-combined classifiers.
    • Augmenting the source dataset with mixup enhanced the accuracy of motion recognition models.
    • The study demonstrated the effectiveness of mixup in addressing overfitting issues associated with TL in sEMG classification.

    Conclusions:

    • Mixup is an effective data augmentation strategy for improving TL-based sEMG motion recognition, particularly when source data is limited.
    • The findings suggest that data augmentation plays a crucial role in enhancing the robustness and accuracy of wearable-based motion recognition systems.
    • Future research will explore diverse datasets and augmentation methods to further understand data quantity's influence on sEMG recognition.