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...

Celý popis

Podrobná bibliografia

Vydané v:	IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (Online) s. 44 - 55
Hlavní autori:	Tazarv, Ali, Labbaf, Sina, Rahmani, Amir, Dutt, Nikil, Levorato, Marco
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	ACM 01.06.2023
Predmet:	Active learning Biomedical monitoring Data collection E-healt Human factors Instruments lIoT Systems Personalization Predictive models Q-learning Real-time systems
ISSN:	2832-2975
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!