Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation.
Afnan Ahmed CrystalMaria ValeroValentina NinoKatherine H IngramPublished in: Sensors (Basel, Switzerland) (2024)
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.
Keyphrases
- physical activity
- deep learning
- blood glucose
- glycemic control
- human health
- weight loss
- machine learning
- public health
- type diabetes
- healthcare
- risk assessment
- body mass index
- cardiovascular disease
- high throughput
- patient safety
- systematic review
- adipose tissue
- randomized controlled trial
- metabolic syndrome
- health information
- climate change
- sleep quality
- social media
- data analysis