Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers

Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers

IntroductionIntelligent ambulatory tracking can assist in the automatic detection of psychological and emotional states relevant to the mental health changes of professionals with high-stakes job responsibilities, such as healthcare workers. However, well-known differences in the variability of ambu...

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Bibliographic Details
Main Authors: Projna Paromita, Karel Mundnich, Amrutha Nadarajan, Brandon M. Booth, Shrikanth S. Narayanan, Theodora Chaspari
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-06-01
Series:Frontiers in Digital Health
Subjects:
metric learning
Siamese neural network
healthcare workers
well-being
mental health
ambulatory monitoring
Online Access:https://www.frontiersin.org/articles/10.3389/fdgth.2023.1195795/full

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https://www.frontiersin.org/articles/10.3389/fdgth.2023.1195795/full

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