This FYP involves developing a mobile app using Flutter, a cross-platform framework, to monitor and track a user's diet. The app utilizes advanced technologies, such as convolutional neural networks (CNNs), for the classification of food items from user-submitted meal images. The deployment of the model is carried out on Azure, a cloud computing platform, leveraging its scalable and flexible infrastructure. Docker containers are employed to encapsulate the app and its dependencies, ensuring consistency across different environments. The integration of a REST API facilitates seamless communication between the mobile app and the deployed model. Additionally, the app incorporates a CSV file containing nutritional information to calculate the nutrition values of identified food items. The user-friendly interface enables easy input of chronic disease questionnaires and meal data, empowering users to track their daily food intake and receive personalized alerts, thereby promoting healthier e
Objectives
This FYP aims to solve the problem of tracking and monitoring one's diet, which is a critical component of maintaining good health. With the help of image recognition and nutrition analysis, the app will make it easier for users to track their daily food intake and make more informed dietary choices. The app can help users achieve their health goals and prevent chronic health issues such as obesity, diabetes, and heart disease.
Socio-Economic Benefit
The diet monitoring and tracking mobile app, developed using Flutter and incorporating convolutional neural networks (CNNs), offers a multifaceted socio-economic impact. By promoting healthier eating habits and preventing chronic diseases, the app contributes to improved individual well-being, potentially reducing healthcare costs and absenteeism. Its emphasis on nutritional education fosters a more informed and health-conscious society. Additionally, the project fuels growth in the technology industry, creating job opportunities and showcasing innovative solutions. As users adopt healthier lifestyles, the app not only benefits individuals but also has the potential to lead to cost savings, positively impacting both personal finances and economic stability at the household level.
Methodologies
The project will adopt an Agile methodology, utilizing sprints, user stories, and the Scrum framework to ensure iterative development with continuous stakeholder involvement. DevOps practices, including continuous integration/deployment, collaborative development tools, and Infrastructure as Code, will streamline the release process and enhance scalability. User-centric design principles, involving iterative prototyping and prioritizing accessibility, aim to create a responsive and user-friendly diet monitoring app. This hybrid approach ensures adaptability to changing requirements, efficient collaboration, and a focus on delivering a reliable and user-centric product within the project's constraints.
Outcome
The diet monitoring and tracking mobile app aims to revolutionize individuals' approach to health by providing a comprehensive tool for managing dietary habits. Through features like chronic disease questionnaires, real-time meal image classification using convolutional neural networks (CNNs), and personalized alerts, the app empowers users to proactively address health concerns and prevent chronic diseases. With a user-friendly interface and advanced technologies, it not only enhances the overall well-being of users but also contributes to nutritional education, fostering awareness and informed food choices. The anticipated positive socio-economic impact includes potential cost savings for individuals and healthcare systems, as well as job creation and industry growth. Built on Agile and DevOps methodologies, the project emphasizes continuous improvement, ensuring the app's adaptability to evolving user needs and technological advancements. The envisioned outcome is a holistic mobile app that makes a meaningful and sustained positive impact on both individual lives and the broader socio-economic landscape.
