Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

Abstract

The management of type 2 diabetes mellitus (T2DM) is generally not only focused on pharmacological therapy. Medical nutrition therapy is often forgotten by patients for several reasons, such as difficulty determining the right nutritional pattern for themselves, regulating their daily nutritional patterns, or even not heeding nutritional diet recommendations given by doctors. Management of nutritional therapy is one of the important efforts that can be made by diabetic patients to prevent an increase in the complexity of the disease. Setting a diet with proper nutrition will help patients manage a healthy diet. The development of Smart Plate Health to Eat is a technological innovation that helps patients and users know the type of food, weight, and nutrients contained in certain foods. This study involved 50 types of food with a total of 30,800 foods using the YOLOv5s algorithm, where the identification, measurement of weight, and nutrition of food were investigated using a Chenbo load cell weight sensor (1 kg), an HX711 weight weighing A/D module pressure sensor, and an IMX219-160 camera module (waveshare). The results of this study showed good identification accuracy in the analysis of four types of food: rice (58%), braised quail eggs in soy sauce (60%), spicy beef soup (62%), and dried radish (31%), with accuracy for weight and nutrition (100%).

Development of Fishcake Gripping and Classification Automation Process Based on Suction Shape Transformation Gripper

“…The YOLO Model was used for training, similar to the image recognition performed by Salaki Reynaldo Joshua et al [14]. This model can be used to distinguish the fishcake shape.…”

Smart-Watches Assisted Sugar Level Monitoring with Different Activities and Nutrition based on Machine Learning Approaches

“…Studies [5][6][7][8][9][10] focused on type-2 diabetes with different bio-makers and nutrition with different intakes in body behaviours. The Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis.…”

“…These type-2 diabetes detection based on machine learning widely investigated in these studies [5][6][7][8][9][10]. These studies investigated diabetes body glucose based on different biomarkers such as blood pressure, nutrition, obesity, glucose, retinopathy, and other factors.…”

The importance of diabetes mellitus in dental practice

“…Svakako, najvažnija je prevencija ovih komplikacija, i u tom kontekstu potrebno je da u sklopu preproceduralne pripreme za stomatološke intervencije pacijenti dobiju savet o značaju redovnih kontrola kod endokrinologa, pravilnog dijetetskog režima, naročito kada su u pitanju posebno vulnerabilne kategorije pacijenata sa dijabetesom, kao što su deca, trudnice i pacijenti starijeg životnog doba [38][39][40] .…”

“…Certainly, the most important aspect is the prevention of these complications, and in this context, as part of the pre-procedural preparation for dental interventions, patients should receive advice on the importance of regular check-ups with an endocrinologist, maintaining a proper dietary regimen, especially for particularly vulnerable categories of diabetic patients such as children, pregnant women, and elderly patients [38][39][40] .…”