Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10916-019-1520-1 | Journal of Medical Systems |
Keywords | Field | DocType |
Stress, Heart rate variability metrics, Wearable devices, Medical students | Information system,Data mining,Wearable computer,Heart rate variability,Mental health,Eustress,Wearable technology,Burnout,Medicine,Applied psychology,Statistical hypothesis testing | Journal |
Volume | Issue | ISSN |
44 | 2 | 0148-5598 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eliana Silva | 1 | 0 | 0.34 |
| Joyce Aguiar | 2 | 0 | 1.35 |
| Luís Paulo Reis | 3 | 482 | 83.34 |
| Jorge Oliveira E Sá | 4 | 0 | 0.34 |
| Joaquim Gonçalves | 5 | 0 | 0.34 |
| Victor Carvalho | 6 | 0 | 1.35 |