Research Article
Using Intermediate Models and Knowledge Learning to Improve Stress Prediction
@INPROCEEDINGS{10.1007/978-3-319-49622-1_16, author={Alban Maxhuni and Pablo Hernandez-Leal and Eduardo Morales and L. Sucar and Venet Osmani and Angelica Muńoz-Mel\^{e}ndez and Oscar Mayora}, title={Using Intermediate Models and Knowledge Learning to Improve Stress Prediction}, proceedings={Applications for Future Internet. International Summit, AFI 2016, Puebla, Mexico, May 25-28, 2016, Revised Selected Papers}, proceedings_a={AFI360}, year={2017}, month={1}, keywords={Motor activity Stress prediction Smartphones}, doi={10.1007/978-3-319-49622-1_16} }- Alban Maxhuni
Pablo Hernandez-Leal
Eduardo Morales
L. Sucar
Venet Osmani
Angelica Muńoz-Meléndez
Oscar Mayora
Year: 2017
Using Intermediate Models and Knowledge Learning to Improve Stress Prediction
AFI360
Springer
DOI: 10.1007/978-3-319-49622-1_16
Abstract
Motor activity in physical and psychological stress exposure has been studied almost exclusively with self-assessment questionnaires and from reports that derive from human observer, such as verbal rating and simple descriptive scales. However, these methods are limited in objectively quantifying typical behaviour of stress. We propose to use accelerometer data from smartphones to objectively quantify stress levels. Used data was collected in real-world setting, from 29 employees in two different organisations over 5 weeks. To improve classification performance we propose to use . These intermediate models represent the mood state of a person which is used to build the final stress prediction model. In particular, we obtained an accuracy of 78.2 % to classify stress levels.
