Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis

New and emerging non-invasive digital tools, such as eye-tracking, facial expression and physiological biometrics, have been implemented to extract more objective sensory responses by panelists from packaging and, specifically, labels. However, integrating these technologies from different company p...

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Autores principales:	Sigfredo Fuentes, Claudia Gonzalez Viejo, Damir D. Torrico, Frank R. Dunshea
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	areas of interest computer vision sensory analysis eye fixations computer application Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/954d0eae5892471ea1a9ddabed592294
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spelling	oai:doaj.org-article:954d0eae5892471ea1a9ddabed5922942021-11-25T18:58:07ZDigital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis10.3390/s212276411424-8220https://doaj.org/article/954d0eae5892471ea1a9ddabed5922942021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7641https://doaj.org/toc/1424-8220New and emerging non-invasive digital tools, such as eye-tracking, facial expression and physiological biometrics, have been implemented to extract more objective sensory responses by panelists from packaging and, specifically, labels. However, integrating these technologies from different company providers and software for data acquisition and analysis makes their practical application difficult for research and the industry. This study proposed a prototype integration between eye tracking and emotional biometrics using the BioSensory computer application for three sample labels: Stevia, Potato chips, and Spaghetti. Multivariate data analyses are presented, showing the integrative analysis approach of the proposed prototype system. Further studies can be conducted with this system and integrating other biometrics available, such as physiological response with heart rate, blood, pressure, and temperature changes analyzed while focusing on different label components or packaging features. By maximizing data extraction from various components of packaging and labels, smart predictive systems can also be implemented, such as machine learning to assess liking and other parameters of interest from the whole package and specific components.Sigfredo FuentesClaudia Gonzalez ViejoDamir D. TorricoFrank R. DunsheaMDPI AGarticleareas of interestcomputer visionsensory analysiseye fixationscomputer applicationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7641, p 7641 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	areas of interest computer vision sensory analysis eye fixations computer application Chemical technology TP1-1185
spellingShingle	areas of interest computer vision sensory analysis eye fixations computer application Chemical technology TP1-1185 Sigfredo Fuentes Claudia Gonzalez Viejo Damir D. Torrico Frank R. Dunshea Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis
description	New and emerging non-invasive digital tools, such as eye-tracking, facial expression and physiological biometrics, have been implemented to extract more objective sensory responses by panelists from packaging and, specifically, labels. However, integrating these technologies from different company providers and software for data acquisition and analysis makes their practical application difficult for research and the industry. This study proposed a prototype integration between eye tracking and emotional biometrics using the BioSensory computer application for three sample labels: Stevia, Potato chips, and Spaghetti. Multivariate data analyses are presented, showing the integrative analysis approach of the proposed prototype system. Further studies can be conducted with this system and integrating other biometrics available, such as physiological response with heart rate, blood, pressure, and temperature changes analyzed while focusing on different label components or packaging features. By maximizing data extraction from various components of packaging and labels, smart predictive systems can also be implemented, such as machine learning to assess liking and other parameters of interest from the whole package and specific components.
format	article
author	Sigfredo Fuentes Claudia Gonzalez Viejo Damir D. Torrico Frank R. Dunshea
author_facet	Sigfredo Fuentes Claudia Gonzalez Viejo Damir D. Torrico Frank R. Dunshea
author_sort	Sigfredo Fuentes
title	Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis
title_short	Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis
title_full	Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis
title_fullStr	Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis
title_full_unstemmed	Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis
title_sort	digital integration and automated assessment of eye-tracking and emotional response data using the biosensory app to maximize packaging label analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/954d0eae5892471ea1a9ddabed592294
work_keys_str_mv	AT sigfredofuentes digitalintegrationandautomatedassessmentofeyetrackingandemotionalresponsedatausingthebiosensoryapptomaximizepackaginglabelanalysis AT claudiagonzalezviejo digitalintegrationandautomatedassessmentofeyetrackingandemotionalresponsedatausingthebiosensoryapptomaximizepackaginglabelanalysis AT damirdtorrico digitalintegrationandautomatedassessmentofeyetrackingandemotionalresponsedatausingthebiosensoryapptomaximizepackaginglabelanalysis AT frankrdunshea digitalintegrationandautomatedassessmentofeyetrackingandemotionalresponsedatausingthebiosensoryapptomaximizepackaginglabelanalysis
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Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis

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