A model using Inception-attention, adversarial domain adaptation, and contrastive learning reaches 93.54% accuracy in three-class cross-subject muscle fatigue detection from sEMG signals.
A hybrid model for muscle fatigue detection: Integrating time and frequency domain features of semg signals with rpe scale during isometric contraction
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Cross-subject Muscle Fatigue Detection via Adversarial and Supervised Contrastive Learning with Inception-Attention Network
A model using Inception-attention, adversarial domain adaptation, and contrastive learning reaches 93.54% accuracy in three-class cross-subject muscle fatigue detection from sEMG signals.