Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings

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 b...

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Bibliographic Details

Published in:	IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (Online) pp. 44 - 55
Main Authors:	Tazarv, Ali, Labbaf, Sina, Rahmani, Amir, Dutt, Nikil, Levorato, Marco
Format:	Conference Proceeding
Language:	English
Published:	ACM 01.06.2023
Subjects:	Active learning Biomedical monitoring Data collection E-healt Human factors Instruments lIoT Systems Personalization Predictive models Q-learning Real-time systems
ISSN:	2832-2975
Online Access:	Get full text
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