Fuentes, S, Viejo, CG, Torrico, DD et al. (1 more author) (2021) Digital integration and automated assessment of eye-tracking and emotional response data using the biosensory app to maximize packaging label analysis. Sensors, 21 (22). ISSN 1424-8220
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the authors. 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/). |
Keywords: | areas of interest; computer vision; sensory analysis; eye fixations; computer application |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Nov 2021 15:04 |
Last Modified: | 30 Nov 2021 15:04 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/s21227641 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180997 |