Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/9/2958 |
| _version_ | 1811306812229550080 |
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| 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 |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:52:27Z |
| format | Article |
| id | doaj.art-81fcdd9224934ed39a37191d7b02d44c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:52:27Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-81fcdd9224934ed39a37191d7b02d44c2022-12-22T02:53:27ZengMDPI AGSensors1424-82202018-09-01189295810.3390/s18092958s18092958Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory PanelistsSigfredo Fuentes0Claudia Gonzalez Viejo1Damir D. Torrico2Frank R. Dunshea3Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, AustraliaFaculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, AustraliaFaculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, AustraliaFaculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, AustraliaIn 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.http://www.mdpi.com/1424-8220/18/9/2958autonomic nervous systemcomputer vision algorithmsintegrated camera systemnonintrusive biometricssensory evaluation |
| spellingShingle | Sigfredo Fuentes Claudia Gonzalez Viejo Damir D. Torrico Frank R. Dunshea Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists Sensors autonomic nervous system computer vision algorithms integrated camera system nonintrusive biometrics sensory evaluation |
| title | Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists |
| title_full | Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists |
| title_fullStr | Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists |
| title_full_unstemmed | Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists |
| title_short | Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists |
| title_sort | development of a biosensory computer application to assess physiological and emotional responses from sensory panelists |
| topic | autonomic nervous system computer vision algorithms integrated camera system nonintrusive biometrics sensory evaluation |
| url | http://www.mdpi.com/1424-8220/18/9/2958 |
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