Abstract:
This project focuses on developing a comprehensive health and wellness tracking application designed to provide users with actionable insights, personalised goal recommendations and predictive analytics to support long-term behavioural improvement. Unlike many conventional fitness trackers that simply collect and display raw data, this system places strong emphasis on transforming health metrics, such as diet, sleep patterns and physical activity into meaningful and context-aware guidance that encourages users to adopt healthier lifestyle habits. The application is built specifically for Windows operating systems and is developed using C# with Windows Presentation Foundation (WPF) to deliver a modern, intuitive and visually engaging interface. The backend is implemented using .NET Core to ensure high performance, scalability and efficient communication between system components, while SQL Server is used for secure and reliable data management. A key feature of the system is the integration of machine learning models using ML.NET, which analyse historical user data to forecast progress, identify trends and dynamically adjust personalised goals. This enables the application to offer highly tailored and continuously adaptive recommendations that evolve alongside the user’s behaviour, habits and progress over time, ensuring not only relevance to their current wellness needs but also sustained motivation, meaningful engagement and long-term commitment to healthier lifestyle practices. The application also incorporates interactive dashboards with visualisations created using LiveCharts2, allowing users to explore their data through clear, dynamic charts suitable for both casual users and those managing long-term health conditions. By combining advanced technologies, predictive modelling and user-focused design, this project delivers a holistic wellness management tool that not only improves health tracking but also empowers users with intelligent insights to support sustainable lifestyle change.
