BayaHAR: Lightweight Bayesian Few-Shot User Adaptation for On-Device Personalized Human Activity Recognition

arXiv:2606.04798 · cs.LG · Submitted 2026-06-03 · Read on arXiv

cs.LG

Submitted: 2026-06-03

Updated: 2026-09-21

Comments: 7 pages, 4 figures, 3 tables, 2 algorithms

Journal ref: Proceedings of the 2026 ACM International Symposium on Wearable Computers (ISWC '26), 2026

DOI: 10.1145/3830727.3834833

Code: https://github.com/maxbrzr/hyper-har

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 58× in this paper · explained in 1 episode
• Calibration — 40× in this paper · explained in 7 episodes
• Domain Adaptation — 8× in this paper · explained in 5 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Personalization — 7× in this paper · explained in 2 episodes
• Domain Shift — 6× in this paper · explained in 4 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Computational overhead — 4× in this paper · explained in 2 episodes
• Few-Shot Learning — 4× in this paper · explained in 4 episodes
• Sensor-based Human Activity Recognition — 4× in this paper
• Bayesian Inference — 3× in this paper · explained in 5 episodes
• Meta-learning — 3× in this paper · explained in 2 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• Federated Learning — 2× in this paper · explained in 14 episodes
• Adversarial Training — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 9 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Gaussian Mixture Model — 1× in this paper
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• Maximum A Posteriori — 1× in this paper
• Maximum Likelihood Estimate — 1× in this paper
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Test-time Adaptation (TTA) — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Feed-Forward Source-Free Domain Adaptation via Class Prototypes
• NEO: No-Optimization Test-Time Adaptation through Latent Re-Centering
• Tent: Fully Test-time Adaptation by Entropy Minimization
• Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition
• Learning Class-level Prototypes for Few-shot Learning

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