BayaHAR: Lightweight Bayesian Few-Shot User Adaptation for On-Device Personalized Human Activity Recognition
Maximilian Burzer, Till Riedel, Michael Beigl, Tobias Röddiger
ISWC '26· 2026
IMUDOI
Summary
Turns pretrained HAR classifiers into prototypical networks with prior prototypes that keep zero-shot performance and regularize adaptation. Labelled calibration uses closed-form Bayesian prototype estimation. The same idea extends to weak labels (only which activities were performed).
Key facts
- License
- Not stated
Updated: Oct 12, 2026
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