无需复杂训练，3秒数据即可让可穿戴设备精准识别用户动作。
Uncertainty-Aware (Un)Supervised Few-Shot User Adaptation for On-Device Personalized Human Activity Recognition
- 用预训练模型构建原型网络，零样本性能不降反而更稳。
- 仅需3秒校准数据，监督/无监督适配均显著提升准确率。
- 闭式更新机制适合手机等设备端实时个性化，轻量高效。
基于传感器的人体动作识别（HAR）模型常因个体运动差异和传感器位置变化导致性能下降。实际可穿戴系统需轻量、灵活且在少量校准数据下仍可靠的个性化方法。本文提出一种免梯度框架，将预训练的HAR分类器转化为原型网络，利用先验原型保持零样本性能并正则化适配过程。对于有标签校准数据，引入闭式贝叶斯原型估计；对无标签数据也扩展相同原理。仅需每类动作3秒校准数据（单样本），监督适配使宏平均F1提升2.76至33.44个百分点，无监督适配提升0.56至32.13个百分点。由于仅需闭式原型更新，该框架可实现现有HAR模型在设备端高效、鲁棒的个性化部署。
原文摘要 · Abstract (English)
Sensor-based Human Activity Recognition (HAR) models often degrade on unseen users due to domain shifts caused by individual movement patterns and sensor placement. Practical wearable HAR systems therefore require personalization methods that are lightweight, applicable whether calibration data is labeled, unlabeled, or unavailable, and robust under limited calibration. We present a gradient-free framework that repurposes pretrained HAR classifiers as Prototypical Networks using using prior prototypes, which preserve zero-shot performance and regularize adaptation. For labeled calibration, we introduce closed-form Bayesian prototype estimation and extend the same principle to unlabeled calibration. With only 3 seconds of calibration data per activity (one shot), supervised adaptation improves macro-F1 by +2.76 to +33.44 percentage points across four datasets, while unsupervised adaptation improves by +0.56 to +32.13 points. Since adaptation requires only closed-form prototype updates, the framework enables efficient and robust on-device personalization of preexisting HAR classifiers.
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