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Wearables and Machine Learning for Improving Runners’ Motivation from an Affective Perspective

Wearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletes’ performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in mos...

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Autores principales: Baldassarri, Sandra, García de Quirós, Jorge, Beltrán, José Ramón, Álvarez, Pedro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920630/
https://www.ncbi.nlm.nih.gov/pubmed/36772647
http://dx.doi.org/10.3390/s23031608
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author Baldassarri, Sandra
García de Quirós, Jorge
Beltrán, José Ramón
Álvarez, Pedro
author_facet Baldassarri, Sandra
García de Quirós, Jorge
Beltrán, José Ramón
Álvarez, Pedro
author_sort Baldassarri, Sandra
collection PubMed
description Wearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletes’ performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In this paper, we present a wearable and a set of machine learning models that are able to deduce runners’ emotions during their training. The solution is based on the analysis of runners’ electrodermal activity, a physiological parameter widely used in the field of emotion recognition. As part of the DJ-Running project, we have used these emotions to increase runners’ motivation through music. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training.
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spelling pubmed-99206302023-02-12 Wearables and Machine Learning for Improving Runners’ Motivation from an Affective Perspective Baldassarri, Sandra García de Quirós, Jorge Beltrán, José Ramón Álvarez, Pedro Sensors (Basel) Article Wearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletes’ performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In this paper, we present a wearable and a set of machine learning models that are able to deduce runners’ emotions during their training. The solution is based on the analysis of runners’ electrodermal activity, a physiological parameter widely used in the field of emotion recognition. As part of the DJ-Running project, we have used these emotions to increase runners’ motivation through music. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training. MDPI 2023-02-01 /pmc/articles/PMC9920630/ /pubmed/36772647 http://dx.doi.org/10.3390/s23031608 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
Baldassarri, Sandra
García de Quirós, Jorge
Beltrán, José Ramón
Álvarez, Pedro
Wearables and Machine Learning for Improving Runners’ Motivation from an Affective Perspective
title Wearables and Machine Learning for Improving Runners’ Motivation from an Affective Perspective
title_full Wearables and Machine Learning for Improving Runners’ Motivation from an Affective Perspective
title_fullStr Wearables and Machine Learning for Improving Runners’ Motivation from an Affective Perspective
title_full_unstemmed Wearables and Machine Learning for Improving Runners’ Motivation from an Affective Perspective
title_short Wearables and Machine Learning for Improving Runners’ Motivation from an Affective Perspective
title_sort wearables and machine learning for improving runners’ motivation from an affective perspective
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920630/
https://www.ncbi.nlm.nih.gov/pubmed/36772647
http://dx.doi.org/10.3390/s23031608
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