DUM-BEL: Accelerating Personal Workout Training with Artificial Intelligence

Abstract

With the increasing emphasis on personal health and fitness, individuals seek accessible digital tools to track workouts, monitor progress, and maintain consistency in their fitness routines. Many existing solutions are either overly complex, require paid subscriptions, or lack customization for users who prefer independent workout management. This paper presents DUM-BEL, a full-stack mobile application designed specifically for individual users to record, organize, and evaluate their personal fitness activities. The system allows users to log workouts, track exercise progress, and create structured workout plans. Users may design workout routines manually or utilize an AI-assisted workout plan generator that produces structured routines based on user goals and preferences. The application follows a client–server architecture and incorporates modern mobile technologies, including a responsive front-end interface, RESTful backend services, and a cloud-hosted PostgreSQL database powered by Supabase. Functional and usability testing were conducted to evaluate system performance and reliability. The results demonstrate that DUM-BEL provides a flexible and user-centered solution for personal workout tracking and workout planning.