University of Oulu

Siirtola, P.; Röning, J. Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection. Sensors 2020, 20, 4402,

Comparison of regression and classification models for user-independent and personal stress detection

Saved in:
Author: Siirtola, Pekka1; Röning, Juha1
Organizations: 1Biomimetics and Intelligent Systems Group, University of Oulu, P.O. BOX 4500, FI-90014 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
Persistent link:
Language: English
Published: Multidisciplinary Digital Publishing Institute, 2020
Publish Date: 2020-09-25


In this article, regression and classification models are compared for stress detection. Both personal and user-independent models are experimented. The article is based on publicly open dataset called AffectiveROAD, which contains data gathered using Empatica E4 sensor and unlike most of the other stress detection datasets, it contains continuous target variables. The used classification model is Random Forest and the regression model is Bagged tree based ensemble. Based on experiments, regression models outperform classification models, when classifying observations as stressed or not-stressed. The best user-independent results are obtained using a combination of blood volume pulse and skin temperature features, and using these the average balanced accuracy was 74.1% with classification model and 82.3% using regression model. In addition, regression models can be used to estimate the level of the stress. Moreover, the results based on models trained using personal data are not encouraging showing that biosignals have a lot of variation not only between the study subjects but also between the session gathered from the same person. On the other hand, it is shown that with subject-wise feature selection for user-independent model, it is possible to improve recognition models more than by using personal training data to build personal models. In fact, it is shown that with subject-wise feature selection, the average detection rate can be improved as much as 4%-units, and it is especially useful to reduce the variance in the recognition rates between the study subjects.

see all

Series: Sensors
ISSN: 1424-8220
ISSN-E: 1424-8220
ISSN-L: 1424-8220
Volume: 20
Issue: 16
Pages: 1 - 12
Article number: 4402
DOI: 10.3390/s20164402
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: This research is supported by the Business Finland funding for Reboot IoT Factory-project (
Copyright information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (