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Artificial Intelligence for Food Computing and Diet Management
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Artificial Intelligence for Food Computing and Diet Management

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 14460

Special Issue Editors

Dr. Luca Anselma

E-Mail Website
Guest Editor
Dipartimento di Informatica, Università di Torino, Torino, Italy
Interests: artificial intelligence; health informatics; temporal reasoning; temporal databases
Dr. Alessandro Mazzei

E-Mail Website
Co-Guest Editor
Dipartimento di Informatica, Università di Torino, 10149 Torino, Italy
Interests: natural language processing; automatic reasoning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food is one of the basic needs for human beings, and has a central role in their daily life. Today, scientific research focusing on food finds a wide array of application domains, ranging from the health and medical field, to the fields of psychology and agriculture, to that of gastronomy intended in its broader sense. Our highly technological and connected society is heavily based on social media and shared resources and content, and every day an impressive amount of food-related data including pictures, illustrations, videos, recipes, meal-prep hints, food planning methods and supports, and food diaries (the ubiquitous “what I eat in a day/week/etc.”) is  generated and managed throughout the world. Such rich and diverse data offer precious insights and provide significant knowledge in order to analyze and understand many fundamental aspects of our society. Food computing techniques are useful in order to obtain and study data from a variety of different sources, to support activities involving the automatic recognition, retrieval, recommendation, and monitoring of food-related information. Both end users and professionals working in the domains of biology, medicine, gastronomy, agronomy, and other sectors can and should make full use of computational approaches for many different purposes.

Moreover, daily diet is a crucial factor influencing a person’s health. As highlighted by the World Health Organization, this is primarily due to the recent changes in people’s lifestyle. The necessity to embrace a healthy diet has been strongly promoted by the FAO. Rigorously following a healthy diet can sometimes be difficult for several reasons; for example, many people are forced to eat out on a daily basis, relying on a limited choice of foods. Another tricky situation can be experienced during holidays, when high-calorie meals are commonly consumed for several days in a row. The pervasiveness of technology can play a key role in diet management. Thanks to cloud computing technologies, it is now possible to consider mobile devices as sophisticated sensors that enhance human senses and, sometimes, modify them in surprising new ways. Moreover, computers can support healthy eating by guiding and encouraging users to adopt virtuous behaviors. In fact, in recent years there has been a growing interest in using multimedia applications on mobile devices as persuasive technologies. This scenario suggests the possibility of exploiting multimedia tools on mobile devices to provide useful dietary guidelines every day. People need smart systems allowing them to follow a healthy diet and motivate them to compensate for their inevitable dietary transgressions.

We are organizing this Special Issue with the goal of gathering different perspectives on the problem of food computing and diet management. Below there is a non-exhaustive list of topics that could be addressed:

  • Dialogue systems for cooking activities;
  • Natural language and food;
  • Computer-supported human–food interaction;
  • Recommender systems for food and diet;
  • Big data for food;
  • Computational approaches to diet management;
  • Persuasive technologies for diet;
  • Ubiquitous computing for dietary assessment;
  • Computer vision for food detection;
  • Deep learning for food analysis;
  • Wearable sensors for food intake detection;
  • Computerized food composition analysis;
  • Multimedia technologies for eating monitoring;
  • Food image analysis and social media;
  • Food multimedia databases;
  • Assisted self-management of health and disease;
  • ICT technologies for tackling malnutrition and undernutrition;
  • Vision techniques for food quality check.

Submitted articles should not have been previously published or be currently under review in other venues. Papers previously published as part of conference/workshop proceedings can be considered for publication in the Special Issue, provided that they contain at least 40% new content. Authors of such submissions must clearly indicate how the journal version of their paper has been extended in a cover letter to the Guest Editors at the time of submission. Moreover, authors must acknowledge their previous paper in the manuscript and resolve any potential copyright issues prior to submission.

We look forward to your papers!

Dr. Luca Anselma
Dr. Alessandro Mazzei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Dialogue systems for cooking activities
  • Natural language and food
  • Computer-supported human–food interaction
  • Recommender systems for food and diet
  • Big data for food
  • Computational approaches to diet management
  • Persuasive technologies for diet
  • Ubiquitous computing for dietary assessment
  • Computer vision for food detection
  • Deep learning for food analysis
  • Wearable sensors for food intake detection
  • Computerized food composition analysis
  • Multimedia technologies for eating monitoring
  • Food image analysis and social media
  • Food multimedia databases
  • Assisted self-management of health and disease
  • ICT technologies for tackling malnutrition and undernutrition
  • Vision techniques for food quality check.

Published Papers (6 papers)

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Research

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14 pages, 4323 KiB  
Article
Amount Estimation Method for Food Intake Based on Color and Depth Images through Deep Learning
by Dong-seok Lee and Soon-kak Kwon
Sensors 2024, 24(7), 2044; https://doi.org/10.3390/s24072044 - 22 Mar 2024
Viewed by 396
Abstract
In this paper, we propose an amount estimation method for food intake based on both color and depth images. Two pairs of color and depth images are captured pre- and post-meals. The pre- and post-meal color images are employed to detect food types [...] Read more.
In this paper, we propose an amount estimation method for food intake based on both color and depth images. Two pairs of color and depth images are captured pre- and post-meals. The pre- and post-meal color images are employed to detect food types and food existence regions using Mask R-CNN. The post-meal color image is spatially transformed to match the food region locations between the pre- and post-meal color images. The same transformation is also performed on the post-meal depth image. The pixel values of the post-meal depth image are compensated to reflect 3D position changes caused by the image transformation. In both the pre- and post-meal depth images, a space volume for each food region is calculated by dividing the space between the food surfaces and the camera into multiple tetrahedra. The food intake amounts are estimated as the difference in space volumes calculated from the pre- and post-meal depth images. From the simulation results, we verify that the proposed method estimates the food intake amount with an error of up to 2.2%. Full article
(This article belongs to the Special Issue Artificial Intelligence for Food Computing and Diet Management)
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22 pages, 3979 KiB  
Article
Robust Deep Neural Network for Learning in Noisy Multi-Label Food Images
by Roberto Morales, Angela Martinez-Arroyo and Eduardo Aguilar
Sensors 2024, 24(7), 2034; https://doi.org/10.3390/s24072034 - 22 Mar 2024
Viewed by 535
Abstract
Deep networks can facilitate the monitoring of a balanced diet to help prevent various health problems related to eating disorders. Large, diverse, and clean data are essential for learning these types of algorithms. Although data can be collected automatically, the data cleaning process [...] Read more.
Deep networks can facilitate the monitoring of a balanced diet to help prevent various health problems related to eating disorders. Large, diverse, and clean data are essential for learning these types of algorithms. Although data can be collected automatically, the data cleaning process is time-consuming. This study aims to provide the model with the ability to learn even when the data are not completely clean. For this purpose, we extend the Attentive Feature MixUp method to enable its learning on noisy multi-label food data. The extension was based on the hypothesis that during the MixUp phase, when a pair of images are mixed, the resulting soft labels should be different for each ingredient, being larger for ingredients that are mixed with the background because they are better distinguished than when they are mixed with other ingredients. Furthermore, to address data perturbation, the incorporation of the Laplace approximation as a post-hoc method was analyzed. The evaluation of the proposed method was performed on two food datasets, where a notable performance improvement was obtained in terms of Jaccard index and F1 score, which validated the hypothesis raised. With the proposed MixUp, our method reduces the memorization of noisy multi-labels, thereby improving its performance. Full article
(This article belongs to the Special Issue Artificial Intelligence for Food Computing and Diet Management)
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20 pages, 971 KiB  
Article
Food Habits: Insights from Food Diaries via Computational Recurrence Measures
by Amruta Pai and Ashutosh Sabharwal
Sensors 2022, 22(7), 2753; https://doi.org/10.3390/s22072753 - 02 Apr 2022
Cited by 3 | Viewed by 2574
Abstract
Humans are creatures of habit, and hence one would expect habitual components in our diet. However, there is scant research characterizing habitual behavior in food consumption quantitatively. Longitudinal food diaries contributed by app users are a promising resource to study habitual behavior in [...] Read more.
Humans are creatures of habit, and hence one would expect habitual components in our diet. However, there is scant research characterizing habitual behavior in food consumption quantitatively. Longitudinal food diaries contributed by app users are a promising resource to study habitual behavior in food selection. We developed computational measures that leverage recurrence in food choices to describe the habitual component. The relative frequency and span of individual food choices are computed and used to identify recurrent choices. We proposed metrics to quantify the recurrence at both food-item and meal levels. We obtained the following insights by employing our measures on a public dataset of food diaries from MyFitnessPal users. Food-item recurrence is higher than meal recurrence. While food-item recurrence increases with the average number of food-items chosen per meal, meal recurrence decreases. Recurrence is the strongest at breakfast, weakest at dinner, and higher on weekdays than on weekends. Individuals with relatively high recurrence on weekdays also have relatively high recurrence on weekends. Our quantitatively observed trends are intuitive and aligned with common notions surrounding habitual food consumption. As a potential impact of the research, profiling habitual behaviors using the proposed recurrent consumption measures may reveal unique opportunities for accessible and sustainable dietary interventions. Full article
(This article belongs to the Special Issue Artificial Intelligence for Food Computing and Diet Management)
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17 pages, 4952 KiB  
Article
A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation
by Wenyan Jia, Yiqiu Ren, Boyang Li, Britney Beatrice, Jingda Que, Shunxin Cao, Zekun Wu, Zhi-Hong Mao, Benny Lo, Alex K. Anderson, Gary Frost, Megan A. McCrory, Edward Sazonov, Matilda Steiner-Asiedu, Tom Baranowski, Lora E. Burke and Mingui Sun
Sensors 2022, 22(4), 1493; https://doi.org/10.3390/s22041493 - 15 Feb 2022
Cited by 8 | Viewed by 2344
Abstract
Knowing the amounts of energy and nutrients in an individual’s diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable [...] Read more.
Knowing the amounts of energy and nutrients in an individual’s diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl. Full article
(This article belongs to the Special Issue Artificial Intelligence for Food Computing and Diet Management)
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14 pages, 396 KiB  
Article
Food Recipe Ingredient Substitution Ontology Design Pattern
by Agnieszka Ławrynowicz, Anna Wróblewska, Weronika T. Adrian, Bartosz Kulczyński and Anna Gramza-Michałowska
Sensors 2022, 22(3), 1095; https://doi.org/10.3390/s22031095 - 31 Jan 2022
Cited by 7 | Viewed by 4107
Abstract
This paper describes a notion of substitutions in food recipes and their ontology design pattern. We build upon state-of-the-art models for food and process. We also present scenarios and examples for the design pattern. Finally, the pattern is mapped to available and relevant [...] Read more.
This paper describes a notion of substitutions in food recipes and their ontology design pattern. We build upon state-of-the-art models for food and process. We also present scenarios and examples for the design pattern. Finally, the pattern is mapped to available and relevant domain ontologies and made publicly available at the ontologydesignpatterns.org portal. Full article
(This article belongs to the Special Issue Artificial Intelligence for Food Computing and Diet Management)
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Review

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21 pages, 792 KiB  
Review
Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection
by Paulo Alexandre Neves, João Simões, Ricardo Costa, Luís Pimenta, Norberto Jorge Gonçalves, Carlos Albuquerque, Carlos Cunha, Eftim Zdravevski, Petre Lameski, Nuno M. Garcia and Ivan Miguel Pires
Sensors 2022, 22(17), 6443; https://doi.org/10.3390/s22176443 - 26 Aug 2022
Cited by 8 | Viewed by 2322
Abstract
Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, [...] Read more.
Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake. Full article
(This article belongs to the Special Issue Artificial Intelligence for Food Computing and Diet Management)
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