Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers
Abstract
Intelligent 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 ambulatory data across individuals challenge many existing automated approaches seeking to learn a generalizable means of well-being estimation. This paper proposes a novel metric learning technique that improves the accuracy and generalizability of automated well-being estimation by reducing inter-individual variability while preserving the variability pertaining to the behavioral construct.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 09, 2023
- Source ID
- 10.3389/fdgth.2023.1195795
Entities
People
- Amrutha Nadarajan
- Brandon M Booth
- Karel Mundnich
- Projna Paromita
- Shrikanth S. Narayanan
- Theodora Chaspari