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Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists

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Názov: Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists
Autori: Sigfredo Fuentes, Claudia Gonzalez Viejo, Damir D. Torrico, Frank R. Dunshea
Zdroj: Sensors (Basel)
Sensors, Vol 18, Iss 9, p 2958 (2018)
Sensors
Volume 18
Issue 9
Informácie o vydavateľovi: MDPI AG, 2018.
Rok vydania: 2018
Predmety: Biometry, Monitoring, sensory evaluation, Emotions, Video Recording, Blood Pressure, TP1-1185, Autonomic Nervous System, Article, Machine Learning, 0404 agricultural biotechnology, Heart Rate, Photography, Humans, Physiologic, sensors and digital hardware, ANZSRC::4606 Distributed computing and systems software, Monitoring, Physiologic, Principal Component Analysis, integrated camera system, Chemical technology, autonomic nervous system, nonintrusive biometrics, ANZSRC::4009 Electronics, computer vision algorithms, 04 agricultural and veterinary sciences, Cloud Computing, Facial Expression, Self Report, ANZSRC::4008 Electrical engineering, Skin Temperature, 0405 other agricultural sciences
Popis: In sensory evaluation, there have been many attempts to obtain responses from the autonomic nervous system (ANS) by analyzing heart rate, body temperature, and facial expressions. However, the methods involved tend to be intrusive, which interfere with the consumers’ responses as they are more aware of the measurements. Furthermore, the existing methods to measure different ANS responses are not synchronized among them as they are measured independently. This paper discusses the development of an integrated camera system paired with an Android PC application to assess sensory evaluation and biometric responses simultaneously in the Cloud, such as heart rate, blood pressure, facial expressions, and skin-temperature changes using video and thermal images acquired by the integrated system and analyzed through computer vision algorithms written in Matlab®, and FaceReaderTM. All results can be analyzed through customized codes for multivariate data analysis, based on principal component analysis and cluster analysis. Data collected can be also used for machine-learning modeling based on biometrics as inputs and self-reported data as targets. Based on previous studies using this integrated camera and analysis system, it has shown to be a reliable, accurate, and convenient technique to complement the traditional sensory analysis of both food and nonfood products to obtain more information from consumers and/or trained panelists.
Druh dokumentu: Article
Other literature type
Popis súboru: application/pdf; Electronic
Jazyk: English
ISSN: 1424-8220
DOI: 10.3390/s18092958
Prístupová URL adresa: https://www.mdpi.com/1424-8220/18/9/2958/pdf
https://pubmed.ncbi.nlm.nih.gov/30189663
https://doaj.org/article/81fcdd9224934ed39a37191d7b02d44c
https://dblp.uni-trier.de/db/journals/sensors/sensors18.html#FuentesVTD18
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164119/
https://minerva-access.unimelb.edu.au/handle/11343/248263
https://doi.org/10.3390/s18092958
https://findanexpert.unimelb.edu.au/scholarlywork/1347893-development-of-a-biosensory-computer-application-to-assess-physiological-and-emotional-responses-from-sensory-panelists
https://pubmed.ncbi.nlm.nih.gov/30189663/
Rights: CC BY
Prístupové číslo: edsair.doi.dedup.....d4f918d522744557a89b7c38df2a5a1e
Databáza: OpenAIRE
Popis
Abstrakt:In sensory evaluation, there have been many attempts to obtain responses from the autonomic nervous system (ANS) by analyzing heart rate, body temperature, and facial expressions. However, the methods involved tend to be intrusive, which interfere with the consumers’ responses as they are more aware of the measurements. Furthermore, the existing methods to measure different ANS responses are not synchronized among them as they are measured independently. This paper discusses the development of an integrated camera system paired with an Android PC application to assess sensory evaluation and biometric responses simultaneously in the Cloud, such as heart rate, blood pressure, facial expressions, and skin-temperature changes using video and thermal images acquired by the integrated system and analyzed through computer vision algorithms written in Matlab®, and FaceReaderTM. All results can be analyzed through customized codes for multivariate data analysis, based on principal component analysis and cluster analysis. Data collected can be also used for machine-learning modeling based on biometrics as inputs and self-reported data as targets. Based on previous studies using this integrated camera and analysis system, it has shown to be a reliable, accurate, and convenient technique to complement the traditional sensory analysis of both food and nonfood products to obtain more information from consumers and/or trained panelists.
ISSN:14248220
DOI:10.3390/s18092958