| Literature DB >> 31897774 |
Eliana Silva1, Joyce Aguiar2, Luís Paulo Reis3, Jorge Oliveira E Sá1, Joaquim Gonçalves4,5, Victor Carvalho5.
Abstract
There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this paper, we introduce the EuStress Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the students in order to predict burnout. The Information System will use a measuring instrument based on wearable device and machine learning techniques to collect and process stress-related data from the students without their explicit interaction. In the present study, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. We performed different statistical tests in order to develop a complex and intelligent model. Results showed the neural network had the better model fit.Keywords: Heart rate variability metrics; Medical students; Stress; Wearable devices
Mesh:
Year: 2020 PMID: 31897774 DOI: 10.1007/s10916-019-1520-1
Source DB: PubMed Journal: J Med Syst ISSN: 0148-5598 Impact factor: 4.460