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Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall

Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to hig...

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Autores principales: Mattern, Enni, Jackson, Roxanne R., Doshmanziari, Roya, Dewitte, Marieke, Varagnolo, Damiano, Knorn, Steffi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669514/
https://www.ncbi.nlm.nih.gov/pubmed/38002432
http://dx.doi.org/10.3390/bioengineering10111308
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author Mattern, Enni
Jackson, Roxanne R.
Doshmanziari, Roya
Dewitte, Marieke
Varagnolo, Damiano
Knorn, Steffi
author_facet Mattern, Enni
Jackson, Roxanne R.
Doshmanziari, Roya
Dewitte, Marieke
Varagnolo, Damiano
Knorn, Steffi
author_sort Mattern, Enni
collection PubMed
description Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to higher technology readiness levels. In this paper, we delve into the feasibility of emotion recognition beyond controlled laboratory environments. For this reason, we create a minimally-invasive experimental setup by combining emotional recall via autobiographical emotion memory tasks with a user-friendly Empatica wristband measuring blood volume pressure, electrodermal activity, skin temperature, and acceleration. We employ standard practices of feature-based supervised learning and specifically use support vector machines to explore subject dependency through various segmentation methods. We collected data from 45 participants. After preprocessing, using a data set of 134 segments from 40 participants, the accuracy of the classifier after 10-fold cross-validation was barely better than random guessing (36% for four emotions). However, when extracting multiple segments from each emotion task per participant using 10-fold cross-validation (i.e., including subject-dependent data in the training set), the classification rate increased to up to 75% for four emotions but was still as low as 32% for leave-one-subject-out cross-validation (i.e., subject-independent training). We conclude that highly subject-dependent issues might pose emotion recognition.
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spelling pubmed-106695142023-11-11 Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall Mattern, Enni Jackson, Roxanne R. Doshmanziari, Roya Dewitte, Marieke Varagnolo, Damiano Knorn, Steffi Bioengineering (Basel) Article Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to higher technology readiness levels. In this paper, we delve into the feasibility of emotion recognition beyond controlled laboratory environments. For this reason, we create a minimally-invasive experimental setup by combining emotional recall via autobiographical emotion memory tasks with a user-friendly Empatica wristband measuring blood volume pressure, electrodermal activity, skin temperature, and acceleration. We employ standard practices of feature-based supervised learning and specifically use support vector machines to explore subject dependency through various segmentation methods. We collected data from 45 participants. After preprocessing, using a data set of 134 segments from 40 participants, the accuracy of the classifier after 10-fold cross-validation was barely better than random guessing (36% for four emotions). However, when extracting multiple segments from each emotion task per participant using 10-fold cross-validation (i.e., including subject-dependent data in the training set), the classification rate increased to up to 75% for four emotions but was still as low as 32% for leave-one-subject-out cross-validation (i.e., subject-independent training). We conclude that highly subject-dependent issues might pose emotion recognition. MDPI 2023-11-11 /pmc/articles/PMC10669514/ /pubmed/38002432 http://dx.doi.org/10.3390/bioengineering10111308 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
Mattern, Enni
Jackson, Roxanne R.
Doshmanziari, Roya
Dewitte, Marieke
Varagnolo, Damiano
Knorn, Steffi
Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall
title Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall
title_full Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall
title_fullStr Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall
title_full_unstemmed Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall
title_short Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall
title_sort emotion recognition from physiological signals collected with a wrist device and emotional recall
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669514/
https://www.ncbi.nlm.nih.gov/pubmed/38002432
http://dx.doi.org/10.3390/bioengineering10111308
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