Detecting exercise-induced fatigue using thermal imaging and deep learning

Bordallo López, Miguel and Blanco Adán, Carlos Roberto del and García Santos, Narciso (2017). Detecting exercise-induced fatigue using thermal imaging and deep learning. In: "Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2017", 28/11/2017 - 01/12/2017, Montreal, Canadá. ISBN 978-1-5386-1842-4. pp. 1-6. https://doi.org/10.1109/IPTA.2017.8310151.

Description

Title: Detecting exercise-induced fatigue using thermal imaging and deep learning
Author/s:
  • Bordallo López, Miguel
  • Blanco Adán, Carlos Roberto del
  • García Santos, Narciso
Item Type: Presentation at Congress or Conference (Article)
Event Title: Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2017
Event Dates: 28/11/2017 - 01/12/2017
Event Location: Montreal, Canadá
Title of Book: Proceedings of Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2017
Título de Revista/Publicación: PROCEEDINGS OF THE 2017 SEVENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA 2017)
Date: December 2017
ISBN: 978-1-5386-1842-4
ISSN: 2154-512X
Subjects:
Freetext Keywords: Fatigue detection, facial expression, deep learning, thermal imaging
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Fatigue has adverse effects in both physical and cognitive abilities. Hence, automatically detecting exercise-induced fatigue is of importance, especially in order to assist in the planning of effort and resting during exercise sessions. Thermal imaging and facial analysis provide a mean to detect changes in the human body unobtrusively and in variant conditions of pose and illumination. In this context, this paper proposes the automatic detection of exercise-induced fatigue using thermal cameras and facial images, analyzing them using deep convolutional neural networks. Our results indicate that classification of fatigued individuals is possible, obtaining an accuracy that reaches over 80% when utilizing single thermal images.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainAEI/FEDERUnspecifiedUnspecifiedUnspecified
Government of Spainprojects TEC2013-48453 (MR-UHDTV)UnspecifiedUnspecifiedUnspecified
Government of SpainTEC2016-75981 (IVME)UnspecifiedUnspecifiedUnspecified

More information

Item ID: 50853
DC Identifier: http://oa.upm.es/50853/
OAI Identifier: oai:oa.upm.es:50853
DOI: 10.1109/IPTA.2017.8310151
Official URL: https://ieeexplore.ieee.org/document/8310151/
Deposited by: Memoria Investigacion
Deposited on: 29 May 2018 15:59
Last Modified: 29 May 2018 15:59
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