Publicación:
A FAIR evaluation of public datasets for stress detection systems
A FAIR evaluation of public datasets for stress detection systems
No hay miniatura disponible
Fecha
2020
Autores
Cuno A.
Condori-Fernandez N.
Mendoza A.
Lovon W.R.
Título de la revista
Revista ISSN
Título del volumen
Editor
IEEE Computer Society
Proyectos de investigación
Unidades organizativas
Número de la revista
Abstracto
Nowadays, datasets are an essential asset used to train, validate, and test stress detection systems based on machine learning. In this paper, we used two sets of FAIR metrics for evaluating five public datasets for stress detection. Results indicate that all these datasets comply to some extent with the (F)indable, (A)ccessible, and (R)eusable principles, but none with the (I)nteroperable principle these findings contribute to raising awareness on (i) the need for the FAIRness development and improvement of stress datasets, and (ii) the importance of promoting open science in the affective computing community. © 2020 IEEE.
Descripción
Palabras clave
Stress detection,
Datasets,
FAIR principles