The Diabetic Buddy: A Diet Regulator andTracking System for Diabetics
- URL: http://arxiv.org/abs/2101.03203v1
- Date: Fri, 8 Jan 2021 20:03:58 GMT
- Title: The Diabetic Buddy: A Diet Regulator andTracking System for Diabetics
- Authors: Muhammad Usman, Kashif Ahmad, Amir Sohail, Marwa Qaraqe
- Abstract summary: The prevalence of diabetes in the Middle East is 17-20%, which is well above the global average of 8-9%.
This paper presents an automatic way of tracking continuous glucose and food intake of diabetics using off-the-shelf sensors and machine learning.
- Score: 3.7026481341955053
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The prevalence of Diabetes mellitus (DM) in the Middle East is exceptionally
high as compared to the rest of the world. In fact, the prevalence of diabetes
in the Middle East is 17-20%, which is well above the global average of 8-9%.
Research has shown that food intake has strong connections with the blood
glucose levels of a patient. In this regard, there is a need to build automatic
tools to monitor the blood glucose levels of diabetics and their daily food
intake. This paper presents an automatic way of tracking continuous glucose and
food intake of diabetics using off-the-shelf sensors and machine learning,
respectively. Our system not only helps diabetics to track their daily food
intake but also assists doctors to analyze the impact of the food in-take on
blood glucose in real-time. For food recognition, we collected a large-scale
Middle-Eastern food dataset and proposed a fusion-based framework incorporating
several existing pre-trained deep models for Middle-Eastern food recognition.
Related papers
- An adapted large language model facilitates multiple medical tasks in diabetes care [20.096444964141508]
Large language models (LLMs) have shown promise in various healthcare scenarios, but their effectiveness across a diverse range of diabetes tasks remains unproven.
This study introduced a framework to train and validate diabetes-specific LLMs.
arXiv Detail & Related papers (2024-09-20T03:47:54Z) - From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [50.80532910808962]
We present GluFormer, a generative foundation model on biomedical temporal data based on a transformer architecture.
GluFormer generalizes to 15 different external datasets, including 4936 individuals across 5 different geographical regions.
It can also predict onset of future health outcomes even 4 years in advance.
arXiv Detail & Related papers (2024-08-20T13:19:06Z) - NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
Self-reporting methods are often inaccurate and suffer from substantial bias.
Recent work has explored using computer vision prediction systems to predict nutritional information from food images.
This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
arXiv Detail & Related papers (2024-05-13T14:56:55Z) - NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene
Dataset for Dietary Intake Estimation [68.49526750115429]
We introduce NutritionVerse-Real, an open access manually collected 2D food scene dataset for dietary intake estimation.
The NutritionVerse-Real dataset was created by manually collecting images of food scenes in real life, measuring the weight of every ingredient and computing the associated dietary content of each dish.
arXiv Detail & Related papers (2023-11-20T11:05:20Z) - NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake
Estimation [65.47310907481042]
One in four older adults are malnourished.
Machine learning and computer vision show promise of automated nutrition tracking methods of food.
NutritionVerse-3D is a large-scale high-resolution dataset of 105 3D food models.
arXiv Detail & Related papers (2023-04-12T05:27:30Z) - Personalized Understanding of Blood Glucose Dynamics via Mobile Sensor
Data [0.0]
We augment Continuous Blood Glucose (CGM) data with sensor input collected by a smart phone.
This data set is novel in terms of it's size, the inclusion of GPS data, and the fact that it was collected non-intrusively from a free-living patient.
arXiv Detail & Related papers (2023-02-02T20:26:05Z) - Enhancing Food Intake Tracking in Long-Term Care with Automated Food
Imaging and Nutrient Intake Tracking (AFINI-T) Technology [71.37011431958805]
Half of long-term care (LTC) residents are malnourished increasing hospitalization, mortality, morbidity, with lower quality of life.
This paper presents the automated food imaging and nutrient intake tracking (AFINI-T) technology designed for LTC.
arXiv Detail & Related papers (2021-12-08T22:25:52Z) - Vision-Based Food Analysis for Automatic Dietary Assessment [49.32348549508578]
This review presents one unified Vision-Based Dietary Assessment (VBDA) framework, which generally consists of three stages: food image analysis, volume estimation and nutrient derivation.
Deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition.
arXiv Detail & Related papers (2021-08-06T05:46:01Z) - Variable Weights Neural Network For Diabetes Classification [0.0]
We have designed a liquid machine learning approach to detect Diabetes with no cost using deep learning.
Our approach shows a significant improvement in the previous state-of-the-art results.
arXiv Detail & Related papers (2021-02-22T11:08:25Z) - Continuous Glucose Monitoring Prediction [0.0]
Diabetes is one of the deadliest diseases in the world and affects nearly 10 percent of the global adult population.
One major development is a system called continuous blood glucose monitoring (CGM)
arXiv Detail & Related papers (2021-01-04T21:32:20Z) - Diabetes Link: Platform for Self-Control and Monitoring People with
Diabetes [0.13681174239726604]
Diabetes Mellitus (DM) is a chronic disease characterized by an increase in blood glucose (sugar) above normal levels.
Diabetes Link is a comprehensive platform for control and monitoring people with DM.
arXiv Detail & Related papers (2020-10-29T19:59:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.