RestUP - Sleep Quality Monitoring App
An Android application that tracks, analyzes, and classifies sleep quality using a Random Forest machine learning algorithm.
Period: October 2025 – June 2026
RestUP is my Informatics thesis project focused on helping users understand their sleep quality through a practical Android experience. The application combines sleep activity tracking, a guided daily check-in, classification results, weekly statistics, and educational content in one product flow.
My contribution
Independent Informatics thesis: I built the Kotlin tracking and assessment flow, used Firebase for application data, and evaluated a Random Forest classifier with Scikit-Learn.
Engineering decisions
The central challenge was turning sleep activity and self-assessment data into a classification that remained understandable and useful inside a mobile interface.
I designed a guided tracking and assessment flow in Kotlin, used Firebase for application data, and evaluated a Random Forest model with Scikit-Learn. The final model reached 92.06% classification accuracy.
92.06% refers to the classification accuracy reported in the thesis evaluation. It describes model performance, rather than a measured improvement in users’ sleep.