SousChef: Mobile Meal Recommender System for Older Adults

Ribeiro, David; Machado, João Carlos; Ribeiro, Jorge Pinto; Vasconcelos, Maria João M.; Vieira, Emília; Barros, Ana Correia de

doi:10.5220/0006281900360045

Cited by 39 publications

(19 citation statements)

References 10 publications

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“…Besides, whether users can develop healthy diet habits also requires longer observation. Now, some other works had a beneficial effect on healthy behavior intervention [27][28][29][30][31]. Elements of gamification, social networks, and participation of experts or teachers were added to these systems to keep users on a healthy diet.…”

Section: Discussionmentioning

confidence: 99%

“…It has been developed for iOS and Android systems, and a survey comprising seven simple questions enabled the app to be evaluated on a user level by considering aspects such as its usefulness and ease of use. One work presented SousChef, a mobile meal recommender system to assist older adults by providing a nutrition companion to guide them in making wise decisions regarding food management and healthy eating habits [28]. The personalized nutritional plans provided by the system according to the information provided by the user, namely, their personal preferences, activity level, and anthropometric measurements.…”

Section: State Of the Artmentioning

confidence: 99%

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Knowledge Management in Healthcare Sustainability: A Smart Healthy Diet Assistant in Traditional Chinese Medicine Culture

Yang

et al. 2018

Sustainability

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In the past 40 years, with the changes to dietary structure and the dramatic increase in the consumption of meat products in developing countries, especially in China, encouraging populations to maintain their previous healthy eating patterns will have health, environmental, and economic co-benefits. Healthy diet education plays an important role in the promotion of people's healthy behavior. However, in the modern age, the data regarding healthy diets available on the internet is increasing rapidly and is distributed on multiple sources. It is time-consuming for users to learn about healthy diets on the internet: they need to search data on multiple platforms, choose and integrate information, and then understand what they have learned. To help people retrieve and learn healthy diet knowledge more efficiently and comprehensively, this paper designs a knowledge graph to integrate healthy diet information on the internet and provides a semantic retrieval system. In the knowledge graph, five main concepts are defined, including food material, dish, nutritional element, symptom, and crowd, as well as the relationships among them. In addition, Chinese dietary culture elements and traditional Chinese medicine (TCM) theory are also contained in the knowledge graph. The preliminary results show that by using the system, users learn healthy diet knowledge more quickly and comprehensively and they are more inclined to have balanced diets. This work could be regarded as a retrieval and education tool, which can assist healthcare and national sustainable development.

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Section: Discussionmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Knowledge Management in Healthcare Sustainability: A Smart Healthy Diet Assistant in Traditional Chinese Medicine Culture

Yang

et al. 2018

Sustainability

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show abstract

“…Knowledge can be acquired from different sources such as experts, book and documents [28] [25]. The previous study had conducted knowledge acquisition in various techniques such as interview the domain expert, review the literature, document, guideline or related web site and observation [17], [25], [27], [29], [30]. A combination of interview and observation is recommended for acquired tacit and explicit knowledge [28].…”

Section: Related Workmentioning

confidence: 99%

iDietScoreᵀᴹ: Meal Recommender System for Athletes and Active Individuals

Mustafa¹,

Rahman²,

Sani³

et al. 2020

IJACSA

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“…The provided results of a user study showed a 73% acceptance rate for the proposed recommender, compared to 51% for the baseline. Ribeiro et al [41] proposed a meal recommender that first estimates the nutritional requirements of the user, then filters the available food items based on several rules and finally scales the recipe ingredients to match the caloric needs of the user. Nutritional requirements are calculated through userprovided information, such as age, sex, weight, height and activity level.…”

Section: Ai Nutrition Recommender Systemsmentioning

confidence: 99%

A survey on AI nutrition recommender systems

Theodoridis

Solachidis

Dimitropoulos

et al. 2019

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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The goal of this work is to provide an overview of existing approaches regarding AI nutrition recommender systems. A breakdown of such systems into task-specific components is presented, as well as methodologies concerned with each individual component. The components of an idealized AI nutrition recommender system are presented and compared to state-of-the-art approaches in the corresponding area of research. Finally, identified issues in some of these areas are also discussed. CCS CONCEPTS • General and reference → Surveys and overviews; • Information systems → Recommender systems; • Computing methodologies → Object recognition; Machine learning.

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SousChef: Mobile Meal Recommender System for Older Adults

Cited by 39 publications

References 10 publications

Knowledge Management in Healthcare Sustainability: A Smart Healthy Diet Assistant in Traditional Chinese Medicine Culture

Knowledge Management in Healthcare Sustainability: A Smart Healthy Diet Assistant in Traditional Chinese Medicine Culture

iDietScoreᵀᴹ: Meal Recommender System for Athletes and Active Individuals

A survey on AI nutrition recommender systems

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