• Version
• Download 8
• File Size 336.55 KB
• File Count 1
• Create Date 11 May 2025
• Last Updated 11 May 2025

ExeRMender: Smart Workout Guidance Through Predictive Modeling And Web Technologies

Authors:

Nilamjyoti Changmai, P Guru Saran, Mithun P, Goutham S, Mahesh T R

Abstract - In today’s digital-first society, physical fitness solutions are rapidly transitioning from traditional gyms to home-based digital platforms. The ExeRMender project introduces a personalized fitness recommendation system that delivers tailored workout suggestions based on user-specific criteria such as targeted muscle groups, equipment availability, and difficulty level. Leveraging Natural Language Processing (NLP) techniques like Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity, the system maps user inputs to a structured dataset of exercises. Built with Flask, Python, and MongoDB, the system’s modular architecture ensures scalability, real-time recommendations, and broad accessibility. Unlike static fitness apps, ExeRMender adapts dynamically to each user’s goals, providing an intelligent alternative to conventional workout planning. This paper outlines the motivation, related research, system design, implementation, and the practical implications of deploying an AI-driven fitness engine in modern wellness ecosystems.

Keywords: Personalized Fitness Recommendation, Machine Learning in Health, Home Workout System, Web-Based Fitness Platform, Flask Application, Predictive Modeling, Health Tech, Fitness Automation, AI-Powered Exercise Planning, Human-Centered Design, Intelligent Workout Suggestions, Health Informatics, Exercise Recommendation System, Fitness Tracking, Web Development for Health, Fitness Goal Optimization, Data-Driven Fitness, Smart Health Applications, Digital Health Intervention, Exercise Routine Generator, Virtual Fitness Assistant, Interactive User Interface, Physical Activity Guidance, Scalable     Health Solutions, Health & Wellness Technology.