Most existing sensor-based monitoring frameworks presume that a large
available labeled dataset is processed to train accurate detection models.
However, in settings where personalization is necessary at deployment time to
fine-tune the model, a person-specific dataset needs to be collected online by
interacting with the users. Optimizing the collection of labels in such phase
is instrumental to impose a tolerable burden on the users while maximizing
personal improvement. In this paper, we consider a fine-grain stress detection
problem based on wearable sensors targeting everyday settings, and propose a
novel context-aware active learning strategy capable of jointly maximizing the
meaningfulness of the signal samples we request the user to label and the
response rate. We develop a multilayered sensor-edge-cloud platform to
periodically capture physiological signals and process them in real-time, as
well as to collect labels and retrain the detection model. We collect a large
dataset and show that the context-aware active learning technique we propose
achieves a desirable detection performance using 88\% and 32\% fewer queries
from users compared to a randomized strategy and a traditional active learning
strategy, respectively.

本文提出了一种上下文感知的主动学习策略，并开发了一个多层传感器边缘云平台，以捕获生理信号并即时处理，以及收集标签并重新训练检测模型。结果表明，所提出的上下文感知主动学习技术相对于基于随机策略和传统主动学习策略，在使用者查询方面分别减少 88％和 32％，同时达到理想的检测性能。