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PACE-Net is a spatio-spectral few-shot calibration framework for wearable sEMG recognition under heterogeneous cross-device transfer. It converts multichannel sEMG into continuous-wavelet time-frequency representations and combines coordinate attention, relaxed-margin metric learning, frozen-statistic adaptation, and test-time soft voting to improve discrimination and stabilize calibration from few labeled samples.
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