• NILESH R. MISHRA
• MS. ZAREEN SHAIKH
• ADITYA MISHRA

The increasing use of mobile health applications has resulted in the widespread generation of personal fitness and nutrition data. Although many applications provide calorie tracking and health monitoring features, interpreting nutritional information and maintaining consistent diet habits remains difficult for non-technical users. Understanding calorie balance, body metrics, and dietary patterns from raw health data is a challenge for many individuals. This paper presents SnackTrack, a user-centric nutrition and diet tracking application designed to transform raw health inputs into structured, visual, and actionable fitness insights. SnackTrack enables users to calculate BMI and TDEE, track diet and workouts, and receive AI-assisted guidance through intuitive dashboards. Unlike traditional health apps that rely on complex long-term analytics, SnackTrack focuses on simple, real-time tracking to deliver immediate clarity and usability. The system emphasizes simplicity, privacy, and visualization-driven design, thereby reducing cognitive load and supporting informed health decision-making.

Mobile Health, Nutrition Tracking, Artificial Intelligence, Fitness Application, Data Visualization, User-Centric Design

"SNACKTRACK: A JAVA-BASED AI NUTRITION APP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 2, page no.c130-c133, February-2026, Available :http://www.jetir.org/papers/JETIR2602221.pdf

"SNACKTRACK: A JAVA-BASED AI NUTRITION APP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 2, page no. ppc130-c133, February-2026, Available at : http://www.jetir.org/papers/JETIR2602221.pdf