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Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors

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 fro...

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Autores principales: Siirtola, Pekka, Tamminen, Satu, Chandra, Gunjan, Ihalapathirana, Anusha, Röning, Juha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920941/
https://www.ncbi.nlm.nih.gov/pubmed/36772638
http://dx.doi.org/10.3390/s23031598
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author Siirtola, Pekka
Tamminen, Satu
Chandra, Gunjan
Ihalapathirana, Anusha
Röning, Juha
author_facet Siirtola, Pekka
Tamminen, Satu
Chandra, Gunjan
Ihalapathirana, Anusha
Röning, Juha
author_sort Siirtola, Pekka
collection PubMed
description 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 [Formula: see text]-score = 0.71) and arousal (mean square error = 0.59 and [Formula: see text]-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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spelling pubmed-99209412023-02-12 Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors Siirtola, Pekka Tamminen, Satu Chandra, Gunjan Ihalapathirana, Anusha Röning, Juha Sensors (Basel) Article 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 [Formula: see text]-score = 0.71) and arousal (mean square error = 0.59 and [Formula: see text]-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. MDPI 2023-02-01 /pmc/articles/PMC9920941/ /pubmed/36772638 http://dx.doi.org/10.3390/s23031598 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Siirtola, Pekka
Tamminen, Satu
Chandra, Gunjan
Ihalapathirana, Anusha
Röning, Juha
Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors
title Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors
title_full Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors
title_fullStr Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors
title_full_unstemmed Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors
title_short Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors
title_sort predicting emotion with biosignals: a comparison of classification and regression models for estimating valence and arousal level using wearable sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920941/
https://www.ncbi.nlm.nih.gov/pubmed/36772638
http://dx.doi.org/10.3390/s23031598
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