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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Veröffentlicht in:	IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (Online) S. 44 - 55
Hauptverfasser:	Tazarv, Ali, Labbaf, Sina, Rahmani, Amir, Dutt, Nikil, Levorato, Marco
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 01.06.2023
Schlagworte:	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-Zugang:	Volltext
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