Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection
Journal
Sensors
Date Issued
2022-08-26
Author(s)
Neves, Paulo Alexandre
Simões, João
Costa, Ricardo
Pimenta, Luís
Gonçalves, Norberto Jorge
Albuquerque, Carlos
Cunha, Carlos
Garcia, Nuno M
Pires, Ivan Miguel
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, 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.
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.
Subjects
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