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- W4367692758 abstract "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." @default.
- W4367692758 created "2023-05-03" @default.
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- W4367692758 date "2023-04-28" @default.
- W4367692758 modified "2023-09-26" @default.
- W4367692758 title "Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings" @default.
- W4367692758 doi "https://doi.org/10.1145/3580252.3586979" @default.
- W4367692758 hasPublicationYear "2023" @default.
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