Coverage-Aware Virtual IMU Augmentation for Low-Resource Human Activity Recognition

arXiv:2609.16768 · cs.AI · Submitted 2026-09-15 · Read on arXiv

cs.AI

Submitted: 2026-09-15

Updated: 2026-09-15

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

Terminology

• Consistency — 16× in this paper · explained in 3 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Ablation — 5× in this paper · explained in 1 episode
• Generalization — 5× in this paper · explained in 13 episodes
• Distillation — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Ablation Studies — 2× in this paper · explained in 1 episode
• Anchor Source — 2× in this paper · explained in 1 episode
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Empirical Coverage — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes

Sources

• MGD$^3$: Mode-Guided Dataset Distillation using Diffusion Models
• Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques
• BenchHAR: Benchmarking Self-Supervised Learning for Generalizable Sensor-based Activity Recognition
• Reducing a complex two-sided smartwatch examination for Parkinson's Disease to an efficient one-sided examination preserving machine learning accuracy

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