Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic re-calibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this re-calibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and online datasets. Overall, SCADANN is shown to systematically improve classifiers' performance over no adaptation and ranks first on almost all the cases tested .
Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals when multiple days have elapsed between each recording by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN) . SCADANN is ranked against three state-of-the-art domain adversarial algorithms and two state-of-the-art self-calibrating algorithms developed specifically for EMG-based deep network adaptation on both offline and dynamic datasets. Overall, SCADANN is shown to systematically improve classifiers' performance over no adaptation and ranks first on almost all the tested cases .
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re-calibration
recalibration
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re-calibration
recalibration
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adapting to
unsupervised adaptation of
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, by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm
by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN)
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state of the art
state-of-the-art
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a multiple-vote
two state-of-the-art
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algorithm
algorithms developed specifically for EMG-based deep network adaptation
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online
dynamic
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cases tested
tested cases