Abstract
Human Activity Recognition (HAR) using wearable devices has advanced significantly in recent years, yet its generalization remains limited when models are deployed to new users. This degradation in performance is primarily due to user-induced concept drift (UICD), highlighting the importance of efficient personalization. In this paper, we present a hybrid framework that first generalizes across users and then rapidly adapts to individual users using few-shot learning directly on-device. By updating only the classifier layer with user-specific data, our method achieves robust personalization with minimal computational and memory overhead. We implement this framework on the energy-efficient RISC-V-based GAP9 microcontroller and validate it across three diverse HAR scenarios: RecGym, QVAR-Gesture, and Ultrasound-Gesture. Post-deployment adaptation yields consistent accuracy improvements of 3.73\%, 17.38\%, and 3.70\% respectively. These results confirm that fast, lightweight, and effective personalization is feasible on embedded platforms, paving the way for scalable and user-aware HAR systems in the wild \footnote{this https URL}.
Abstract (translated)
近年来，使用可穿戴设备进行人体活动识别（HAR）技术取得了显著进步，然而当模型部署到新用户时，其泛化能力依然有限。这种性能下降主要是由于由用户引起的概念漂移（UICD），这突显了高效个性化的必要性。在本文中，我们提出了一种混合框架，该框架首先使模型能够在不同用户之间进行泛化，然后通过设备上直接使用少量样本学习快速适应个体用户。我们的方法仅更新分类器层，并结合用户特定数据以实现稳健的个性化，同时保持计算和内存开销最小。 我们在基于低功耗RISC-V架构的GAP9微控制器上实现了这一框架，并在三个不同的HAR场景中进行了验证：RecGym、QVAR-Gesture和Ultrasound-Gesture。部署后的适应性改进分别带来了3.73%，17.38% 和 3.70% 的准确性提升。 这些结果证实了快速、轻量且有效的个性化策略在嵌入式平台上是可行的，为可穿戴设备中大规模和用户感知HAR系统的实施铺平了道路。\[1\] 参考文献： \[1\] this https URL
URL
https://arxiv.org/abs/2508.15413
