Fitmate

FitMate - Your Personal AI Fitness Companion The research paper presents FitMate, an AI-driven chatbot designed to provide personalized fitness and nutrition guidance. The paper discusses how artificial intelligence is transforming digital health by offering tailored workout and diet recommendations, previously accessible only through personal trainers and nutritionists. Key Highlights: Problem Statement: There is a lack of affordable and truly personalized fitness apps that consider user-specific needs like BMI, dietary preferences, and workout goals. Objective: FitMate is developed to bridge this gap by utilizing AI, web development, and NLP to create a chatbot capable of real-time fitness coaching. Methodology: The system integrates OpenAI’s GPT-4 model to generate intelligent workout and diet plans based on user inputs such as weight, height, age, and fitness goals. System Architecture: Frontend: Built using HTML and Tailwind CSS for a responsive design. Backend: Developed with Node.js and Express, handling user queries and API communication. AI Integration: Uses GPT-4 for generating personalized fitness advice. Error Handling & Scalability: Ensures smooth operation through input validation, API failure handling, and future database integration (MongoDB). Results & Findings: High User Engagement: Users actively interacted with FitMate for BMI calculations and fitness advice. Accurate BMI Calculation: Achieved 100% accuracy in BMI classification, helping users receive suitable recommendations. Diet Plan Effectiveness: 75% of users adhered to the personalized diet plans, with improvements in protein and vegetable consumption. Scalability Potential: The system can be expanded to integrate with wearable devices and store user history for enhanced personalization. Conclusion & Future Scope: FitMate demonstrates AI’s potential in revolutionizing fitness management by making personalized health guidance affordable and accessible. Future improvements include real-time health tracking, AI-powered progress monitoring, and expansion of fitness databases. This research contributes to the growing field of AI-powered fitness coaching, showcasing how AI-driven chatbots can enhance health, fitness adherence, and user engagement.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-26
DOI
https://doi.org/10.5281/zenodo.15091754
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fitmate

Zenodo (CERN European Organization for Nuclear Research)
AI in Service Interactions
article

Fitmate

article en

Abstract

FitMate - Your Personal AI Fitness Companion The research paper presents FitMate, an AI-driven chatbot designed to provide personalized fitness and nutrition guidance. The paper discusses how artificial intelligence is transforming digital health by offering tailored workout and diet recommendations, previously accessible only through personal trainers and nutritionists. Key Highlights: Problem Statement: There is a lack of affordable and truly personalized fitness apps that consider user-specific needs like BMI, dietary preferences, and workout goals. Objective: FitMate is developed to bridge this gap by utilizing AI, web development, and NLP to create a chatbot capable of real-time fitness coaching. Methodology: The system integrates OpenAI’s GPT-4 model to generate intelligent workout and diet plans based on user inputs such as weight, height, age, and fitness goals. System Architecture: Frontend: Built using HTML and Tailwind CSS for a responsive design. Backend: Developed with Node.js and Express, handling user queries and API communication. AI Integration: Uses GPT-4 for generating personalized fitness advice. Error Handling & Scalability: Ensures smooth operation through input validation, API failure handling, and future database integration (MongoDB). Results & Findings: High User Engagement: Users actively interacted with FitMate for BMI calculations and fitness advice. Accurate BMI Calculation: Achieved 100% accuracy in BMI classification, helping users receive suitable recommendations. Diet Plan Effectiveness: 75% of users adhered to the personalized diet plans, with improvements in protein and vegetable consumption. Scalability Potential: The system can be expanded to integrate with wearable devices and store user history for enhanced personalization. Conclusion & Future Scope: FitMate demonstrates AI’s potential in revolutionizing fitness management by making personalized health guidance affordable and accessible. Future improvements include real-time health tracking, AI-powered progress monitoring, and expansion of fitness databases. This research contributes to the growing field of AI-powered fitness coaching, showcasing how AI-driven chatbots can enhance health, fitness adherence, and user engagement.

Zenodo (CERN European Organization for Nuclear Research)
Zero hunger
Openalex Percentile: Top 75%
AI in Service Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.