University of Oulu

Siirtola, P.; Tamminen, S.; Chandra, G.; Ihalapathirana, A.; Röning, J. Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors. Sensors 2023, 23, 1598.

Predicting emotion with biosignals : a comparison of classification and regression models for estimating valence and arousal level using wearable sensors

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Author: Siirtola, Pekka1; Tamminen, Satu1; Chandra, Gunjan1;
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, 3.5 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2023
Publish Date: 2023-08-09


This study aims to predict emotions using biosignals collected via wrist-worn sensor and evaluate the performance of different prediction models. Two dimensions of emotions were considered: valence and arousal. The data collected by the sensor were used in conjunction with target values obtained from questionnaires. A variety of classification and regression models were compared, including Long Short-Term Memory (LSTM) models. Additionally, the effects of different normalization methods and the impact of using different sensors were studied, and the way in which the results differed between the study subjects was analyzed. The results revealed that regression models generally performed better than classification models, with LSTM regression models achieving the best results. The normalization method called baseline reduction was found to be the most effective, and when used with an LSTM-based regression model it achieved high accuracy in detecting valence (mean square error = 0.43 and 𝑅2-score = 0.71) and arousal (mean square error = 0.59 and 𝑅2-score = 0.81). Moreover, it was found that even if all biosignals were not used in the training phase, reliable models could be obtained; in fact, for certain study subjects the best results were obtained using only a few of the sensors.

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Series: Sensors
ISSN: 1424-8220
ISSN-E: 1424-8220
ISSN-L: 1424-8220
Volume: 23
Issue: 3
Article number: 1598
DOI: 10.3390/s23031598
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This research received no external funding.
Copyright information: © 2023 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 (