Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams

Nutrition and social interactions are both key aspects of the daily lives of humans. In this work, we propose a system to evaluate the influence of social interaction in the nutritional habits of a person from a first-person perspective. In order to detect the routine of an individual, we construct...

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Authors: Glavan, Andreea, Matei, Alina, Radeva, Petia, Talavera Martínez, Estefanía
Format: article
Status:Versión aceptada para publicación
Publication Date:2020
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/194915
Online Access:https://hdl.handle.net/2445/194915
Access Level:Open access
Keyword:Nutrició
Reconeixement de formes (Informàtica)
Visió per ordinador
Aprenentatge automàtic
Nutrition
Pattern recognition systems
Computer vision
Machine learning
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spelling Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streamsGlavan, AndreeaMatei, AlinaRadeva, PetiaTalavera Martínez, EstefaníaNutricióReconeixement de formes (Informàtica)Visió per ordinadorAprenentatge automàticNutritionPattern recognition systemsComputer visionMachine learningNutrition and social interactions are both key aspects of the daily lives of humans. In this work, we propose a system to evaluate the influence of social interaction in the nutritional habits of a person from a first-person perspective. In order to detect the routine of an individual, we construct a nutritional behaviour pattern discovery model, which outputs routines over a number of days. Our method evaluates similarity of routines with respect to visited food-related scenes over the collected days, making use of Dynamic Time Warping, as well as considering social engagement and its correlation with food-related activities. The nutritional and social descriptors of the collected days are evaluated and encoded using an LSTM Autoencoder. Later, the obtained latent space is clustered to find similar days unaffected by outliers using the Isolation Forest method. Moreover, we introduce a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100 k egocentric images gathered by 7 users. Several different visualizations are evaluated for the understanding of the findings. Our results demonstrate good performance and applicability of our proposed model for social-related nutritional behaviour understanding. At the end, relevant applications of the model are discussed by analysing the discovered routine of particular individuals.Elsevier2023202320202023info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion43 p.application/pdfhttps://hdl.handle.net/2445/194915Articles publicats en revistes (Matemàtiques i Informàtica)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://doi.org/10.1016/j.eswa.2020.114506Expert Systems with Applications, 2020, vol. 171https://doi.org/10.1016/j.eswa.2020.114506cc-by-nc-nd (c) Elsevier, 2020https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1949152026-05-29T05:05:01Z
dc.title.none.fl_str_mv Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams
title Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams
spellingShingle Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams
Glavan, Andreea
Nutrició
Reconeixement de formes (Informàtica)
Visió per ordinador
Aprenentatge automàtic
Nutrition
Pattern recognition systems
Computer vision
Machine learning
title_short Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams
title_full Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams
title_fullStr Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams
title_full_unstemmed Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams
title_sort Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams
dc.creator.none.fl_str_mv Glavan, Andreea
Matei, Alina
Radeva, Petia
Talavera Martínez, Estefanía
author Glavan, Andreea
author_facet Glavan, Andreea
Matei, Alina
Radeva, Petia
Talavera Martínez, Estefanía
author_role author
author2 Matei, Alina
Radeva, Petia
Talavera Martínez, Estefanía
author2_role author
author
author
dc.subject.none.fl_str_mv Nutrició
Reconeixement de formes (Informàtica)
Visió per ordinador
Aprenentatge automàtic
Nutrition
Pattern recognition systems
Computer vision
Machine learning
topic Nutrició
Reconeixement de formes (Informàtica)
Visió per ordinador
Aprenentatge automàtic
Nutrition
Pattern recognition systems
Computer vision
Machine learning
description Nutrition and social interactions are both key aspects of the daily lives of humans. In this work, we propose a system to evaluate the influence of social interaction in the nutritional habits of a person from a first-person perspective. In order to detect the routine of an individual, we construct a nutritional behaviour pattern discovery model, which outputs routines over a number of days. Our method evaluates similarity of routines with respect to visited food-related scenes over the collected days, making use of Dynamic Time Warping, as well as considering social engagement and its correlation with food-related activities. The nutritional and social descriptors of the collected days are evaluated and encoded using an LSTM Autoencoder. Later, the obtained latent space is clustered to find similar days unaffected by outliers using the Isolation Forest method. Moreover, we introduce a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100 k egocentric images gathered by 7 users. Several different visualizations are evaluated for the understanding of the findings. Our results demonstrate good performance and applicability of our proposed model for social-related nutritional behaviour understanding. At the end, relevant applications of the model are discussed by analysing the discovered routine of particular individuals.
publishDate 2020
dc.date.none.fl_str_mv 2020
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/194915
url https://hdl.handle.net/2445/194915
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1016/j.eswa.2020.114506
Expert Systems with Applications, 2020, vol. 171
https://doi.org/10.1016/j.eswa.2020.114506
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Elsevier, 2020
https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by-nc-nd (c) Elsevier, 2020
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 43 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Articles publicats en revistes (Matemàtiques i Informàtica)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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