中文标题#
通過設備端少樣本學習在可穿戴人體活動識別中連接泛化與個性化
英文标题#
Bridging Generalization and Personalization in Wearable Human Activity Recognition via On-Device Few-Shot Learning
中文摘要#
人體活動識別（HAR）使用可穿戴設備需要在不同用戶之間具有強大的泛化能力，並且對個體進行高效的個性化處理。 然而，傳統的 HAR 模型在面對用戶特定的變化時往往無法泛化，導致性能下降。 為了解決這一挑戰，我們提出了一種新的設備端少樣本學習框架，該框架在可穿戴 HAR 中連接了泛化和個性化。 我們的方法首先在用戶之間訓練一個可泛化的表示，然後僅用少量標記樣本快速適應新用戶，在資源受限的設備上直接更新輕量級分類器層。 這種方法實現了計算和內存成本最小的穩健設備端學習，使其適用於實際部署。 我們在節能的 RISC-V GAP9 微控制器上實現了我們的框架，並在三個基準數據集（RecGym、QVAR-Gesture、Ultrasound-Gesture）上進行了評估。 在這些場景中，部署後的適應性分別提高了準確率 3.73%、17.38% 和 3.70%。 這些結果表明，通過無縫結合泛化和個性化，少樣本設備端學習能夠實現可擴展、用戶感知和節能的可穿戴人體活動識別 \footnote {https://github.com/kangpx/onlineTiny2023}
英文摘要#
Human Activity Recognition (HAR) with wearable devices requires both strong generalization across diverse users and efficient personalization for individuals. However, conventional HAR models often fail to generalize when faced with user-specific variations, leading to degraded performance. To address this challenge, we propose a novel on-device few-shot learning framework that bridges generalization and personalization in wearable HAR. Our method first trains a generalizable representation across users and then rapidly adapts to new users with only a few labeled samples, updating lightweight classifier layers directly on resource-constrained devices. This approach achieves robust on-device learning with minimal computation and memory cost, making it practical for real-world deployment. We implement our framework on the energy-efficient RISC-V GAP9 microcontroller and evaluate it on three benchmark datasets (RecGym, QVAR-Gesture, Ultrasound-Gesture). Across these scenarios, post-deployment adaptation improves accuracy by 3.73%, 17.38%, and 3.70%, respectively. These results demonstrate that few-shot on-device learning enables scalable, user-aware, and energy-efficient wearable human activity recognition by seamlessly uniting generalization and personalization \footnote{https://github.com/kangpx/onlineTiny2023}.
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