Thought on food: a systematic review of current approaches and challenges for food intake detection

article

Neves, Paulo Alexandre
Simões, João
Costa, Ricardo
Pimenta, Luís
Gonçalves, Norberto Jorge
Albuquerque, Carlos
Cunha, Carlos
Zdravevski, Eftim
Lameski, Petre
Garcia, Nuno M.
Pires, Ivan Miguel

MDPI

P.A. Neves [et al.] (2022) - Thought on food: a systematic review of current approaches and challenges for food intake detection. Sensors. Vol. 22, nº.17, p. 6443. DOI doi: 10.3390/s22176443.

10.3390/s22176443

Thought on food: a systematic review of current approaches and challenges for food intake detection

Food intake Detection
biosensors
neural networks
image processing
nutrition

COST Action IC1303-AAPELE—Architectures, Algorithms, and Protocols for Enhanced Living Environments and COST Action CA16226–SHELD-ON—Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology)

2022-09-15T12:24:23Z
2022-09-15T12:24:23Z
2022

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.
info:eu-repo/semantics/publishedVersion

openAccess

eng