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

2026-09-15

AI Summary

为了解决低资源条件下人体活动识别数据不足的问题，提出了一种基于覆盖感知的虚拟IMU增强框架，通过选择锚点生成并筛选虚拟样本，提高模型泛化性能。

Abstract

IMU-based human activity recognition (HAR) enables continuous, privacy-friendly monitoring of daily activities using wearable sensors. However, building reliable HAR models that generalize across diverse users and real-world conditions requires large amounts of labeled IMU data, which are expensive and difficult to collect. Existing approaches mainly rely on augmentation or synthesis to expand available data, but indiscriminately adding virtual samples may provide little new coverage and introduce unreliable supervision. To overcome these challenges, we propose a novel coverage-aware virtual IMU augmentation framework that decides where to supplement real data, how to generate and select virtual candidates, and how strongly to weight them during training. Specifically, we select diversity and scarcity anchors in a learned sensor embedding space, convert anchor dynamics into prompts, and generate virtual IMU candidates for each anchor. We then rank candidates by a selection cost combining anchor proximity and label consistency, and incorporate the selected candidates into HAR training with reliability-based weights. Experiments on public HAR benchmarks show that our method consistently improves recognition performance over competitive baselines, and ablation studies confirm the effectiveness of the proposed framework design.

Problem

• IMU-based human activity recognition
• labeled IMU data
• data augmentation
• virtual samples
• unreliable supervision

Innovation

• coverage-aware augmentation
• virtual IMU
• reliable weighting
• anchor selection
• sensor embedding space