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Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation

Authors :
Afnan Ahmed Crystal
Maria Valero
Valentina Nino
Katherine H. Ingram
Source :
Sensors, Vol 24, Iss 13, p 4089 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Diabetes has emerged as a worldwide health crisis, affecting approximately 537 million adults. Maintaining blood glucose requires careful observation of diet, physical activity, and adherence to medications if necessary. Diet monitoring historically involves keeping food diaries; however, this process can be labor-intensive, and recollection of food items may introduce errors. Automated technologies such as food image recognition systems (FIRS) can make use of computer vision and mobile cameras to reduce the burden of keeping diaries and improve diet tracking. These tools provide various levels of diet analysis, and some offer further suggestions for improving the nutritional quality of meals. The current study is a systematic review of mobile computer vision-based approaches for food classification, volume estimation, and nutrient estimation. Relevant articles published over the last two decades are evaluated, and both future directions and issues related to FIRS are explored.

Details

Language :
English
ISSN :
14248220
Volume :
24
Issue :
13
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.ffcd83d22e944358e7e2c8ebfb1cbf4
Document Type :
article
Full Text :
https://doi.org/10.3390/s24134089