Fitness Guide With Mental Health Support Using Fitmind

Syed Ashraf Ali; Mohammed Zaid Burhan Surve; Abdul Wahed Sabri; Neha Unnisa

doi:10.62647/IJITCE2025V13I2sPP567-573

Authors

Syed Ashraf Ali B.E. 4th Year Students, Department of CSE (Artificial Intelligence and Data Science), ISL Engineering College Author
Mohammed Zaid Burhan Surve B.E. 4th Year Students, Department of CSE (Artificial Intelligence and Data Science), ISL Engineering College Author
Abdul Wahed Sabri B.E. 4th Year Students, Department of CSE (Artificial Intelligence and Data Science), ISL Engineering College Author
Neha Unnisa Assistant Professor, Department of Computer Science and Engineering, ISL Engineering College Author

DOI:

https://doi.org/10.62647/IJITCE2025V13I2sPP567-573

Keywords:

Fitness, Mental Health, Flask, Machine Learning, MYSQL

Abstract

This project delivers physical fitness and mental
wellness in today’s fast-paced lifestyle which has
become a crucial part of our life. While there are
numerous mobile and web applications addressing
either fitness or mental health, very few offer a
holistic, intelligent, and integrated solution. The Fit
Mind system is a comprehensive web-based
application designed to promote overall well-being
through the power of artificial intelligence and
machine learning. The platform provides three major
features: a personalized Fitness Planner, a Mental
Health Tracker, and a Meditation & Wellness Advisor,
all powered by robust ML models. The Fitness Planner
Module predicts a user’s fitness category based on
personal parameters like age, BMI, activity level, and
lifestyle habits, and recommends a suitable workout
plan and diet. The Mental Health Tracker Module
leverages psychological screening scales like PHQ-9
(Patient Health Questionnaire-9), GAD-7
(Generalized Anxiety Disorder-7), and DASS-21
(Depression, Anxiety, and Stress Scale) to assess
mental health conditions, and provides tailored advice
for improving mental wellness. The Meditation &
Wellness Module evaluates lifestyle parameters such
as sleep quality, screen time, stress levels, and
mindfulness score, then suggests guided meditations,
breathing techniques, and daily wellness tips.
In addition, the system incorporates an AI-based
Chatbot Module that uses natural language
processing (NLP) and a Naive Bayes classifier to
understand user queries and offer instant responses
related to fitness, mental health, and meditation.
Developed using Python, Flask, and MySQL, Fit Mind
delivers an interactive and user-friendly experience
with real-time recommendations and progress
tracking.
Overall, FitMind serves as a smart virtual wellness
assistant aimed at making mental and physical wellbeing
accessible, personalized, and engaging for all
users, especially students and working professionals.
Its modular design also makes it scalable for future
integration with wearable health devices and mobile
platforms.