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Addressing Gym Progression Inconsistency Through a Tracking Application


Samuel Stanford

07/05/2026

Supervised by Fernando Alva Manchego; Moderated by Christopher Wallbridge

Many gym goers struggle to maintain consistent training progression, without a clear way to track and review their workouts. Current applications are either too basic or too complex, leaving a gap for an accessible, data driven training application. The project is a cross platform mobile gym tracking application, built with React Native and Expo. This project uses offline-first with SQLite so it’s in a gym environment without internet connection. A rulebased recommendation engine analyses the user's training history. It applies progressive overloading principles to suggest weight adjustments and which muscle groups to train. There is also an opt-in AI coaching mode that connects to GPT-4o through the OpenRouter API.


Initial Plan (02/02/2026) [Zip Archive]

Final Report (07/05/2026) [Zip Archive]

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