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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10669514 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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