Review on WorkFit Balance: A Daily Fitness Scheduler

Abstract

The increasing demand for personalized fitness solutions has led to the development of technology-driven applications that cater to individual health goals. This paper presents WorkFit Balance, an AI-driven fitness planning application designed to generate personalized daily plans for exercise and nutrition based on user profiles and schedules. Leveraging machine learning techniques, specifically the K-Nearest Neighbors (KNN) algorithm, the application recommends exercises and meals tailored to the user’s fitness goals, fitness type, and body mass index (BMI). The system integrates with a Django-based web framework for user interaction, allowing seamless input of schedules and preferences. This study reviews the methodologies used in developing WorkFit Balance, including data preprocessing, AI model implementation, and system architecture. The application’s effectiveness is demonstrated through its ability to schedule activities around user availability and provide balanced meal recommendations. Challenges such as limited dataset diversity and real-time processing constraints are discussed, along with potential solutions. By offering a scalable and accessible fitness planning tool, WorkFit Balance aims to promote healthier lifestyles and bridge the gap between technology and personal wellness. Future work will focus on enhancing AI recommendations and integrating user feedback for continuous improvement.

Introduction

The increasing sedentary lifestyle and rising health issues have highlighted the need for personalized, accessible fitness solutions. Traditional fitness plans often lack customization, reducing user adherence. WorkFit Balance is a web-based application that uses AI and machine learning to deliver personalized fitness and nutrition plans tailored to individual user profiles and schedules.

Users input details like age, BMI, fitness goals (weight loss, gain, maintenance), and daily availability. The system employs the K-Nearest Neighbors (KNN) algorithm to recommend suitable exercises and meals based on these inputs, matching user needs with a curated dataset of exercises and nutritional information.

A scheduling algorithm ensures that fitness activities and meals fit into the user's daily routine without conflicts, adjusting times based on preferences and busy periods. Built on the Django framework, WorkFit Balance offers a user-friendly interface with secure profile management, real-time plan generation, and tracking of past plans.

The paper outlines the methodology, system architecture, and challenges, emphasizing how AI enhances fitness planning by making it personalized, practical, and adaptable.

Conclusion

This paper presented WorkFit Balance, an AI-driven fitness planning application that leverages the K-Nearest Neighbors (KNN) algorithm and the Django framework to deliver personalized exercise and nutrition plans. The system effectively integrates user profiles, schedules, and AI recommendations to promote healthier lifestyles. While the application demonstrates promising results in scheduling and personalization, challenges such as limited dataset diversity and the need for more sophisticated meal categorization remain. Future work will focus on enhancing the AI model with larger datasets, incorporating user feedback for adaptive recommendations, and adding features like progress tracking and mobile app support. WorkFit Balance represents a step toward accessible, technology-driven fitness solutions, with the potential to impact personal wellness on a broader scale.

References

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Copyright

Copyright © 2025 Prof. Ankita Tidake, Anurag Amale, Akash Madhavan, Atharva Jadhav, Soham Waghmare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.