Fitaura: AI & Machine Learning Powered Fitness Tracker

Fitaura is a seamless fitness tracker application built using C++, Python-based machine learning, and AI to provide predictive insights into personal fitness. The application tracks key parameters over a 7-day cycle, including daily steps, calorie intake, sleep duration, water consumption, height, weight, and BMI. Using a linear regression model in Python, Fitaura forecasts the next 15 days of fitness trends, enabling users to anticipate patterns and adjust their routines proactively. The application implements major object-oriented programming principles such as inheritance, encapsulation, and polymorphism, ensuring a modular, scalable, and maintainable architecture. Its GUI, built with Qt Widgets, offers an intuitive interface for users to monitor progress through visual graphs and receive personalized recommendations based on predictive analytics, empowering them to make informed decisions about their fitness journeys.

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2. Fitaura is an innovative and user-friendly fitness tracker application developed using C++, machine learning, and
artificial intelligence. It offers users predictive insights into their fitness by monitoring key parameters over a 7-
day period, including daily steps, calorie intake, sleep duration, water consumption, height, weight, and BMI.
Utilizing a linear regression model implemented in Python, Fitaura forecasts the user’s fitness data for the next 15
days, enabling them to anticipate trends and adjust their daily routines accordingly.fit
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Early Detection of Health Trends: Allows users to spot potential health issues in
advance.
Promotion of Healthier Habits: Highlights patterns in sleep, hydration, and activity to
encourage better lifestyle choices.
Motivation Through Data: Offers clear progress tracking and predictions to keep
users engaged.
Proactive Health Management: Helps users manage weight, BMI, and overall fitness
before problems arise.
Interactive Experience: Uses AI and machine learning to create a more intelligent
and engaging fitness app.

3. Problem Statement
What issue or gap our project addresses? Adds machine learning prediction for future fitness trends
Incorporates forecasting of fitness outcomes
Modular architecture with Qt Widgets GUI and CLI interface planned
The integration of synchronization and predictive analytics
provides timely, personalized insights that help users
proactively manage their fitness goals.

4. Objectives To Demonstrate Object-Oriented Programming (OOP) Concepts in
C++
To Collect and Manage Multi-Parameter Fitness Data
To Integrate a Python-Based Machine Learning Based on Linear
Regression Model
To Provide Predictive Feedback and Insightful Reporting
For Free Assistance for Those Who Can’t Afford Professional and
Premium Fitness Plans

5. Literature Review/Background Web-Based Fitness Systems (Patel & Shah, 2019):
Similarity: Fitaura manages user fitness data and adds predictive modeling for future
insights.
Online Health Monitoring Portals (Singh et al., 2020):
Similarity: Fitaura gathers health parameters and goes further by forecasting future
fitness outcomes.
Cloud-Based Personal Fitness Platforms (Kumar & Mehta, 2021):
Similarity: Fitaura supports synchronized fitness data and integrates predictive analytics
for decision support.
Hybrid Web and CLI Fitness Applications (Patel & Gomez, 2020):
Similarity: Fitaura’s modular design plans to offer both GUI (Qt Widgets) and CLI
interfaces with synchronized data and consistent features.

6. Methodology Languages: C++ (core logic,
GUI), Python (ML predictions)
Frameworks: Qt Widgets
(GUI),
QtCore/QtGui/QProcess
Machine Learning: Scikit-
learn (Linear Regression),
pandas, numpy, joblib, json
IDE & Tools: Qt Creator, VS
Code, GitHub Technologies &
Frameworks Used Linear Regression Model
(PolynomialFeatures +
pipeline)
Input: 7-day fitness data
(steps, sleep, water, calories,
etc.)
Output: 15-day predictions +
recommendations
Algorithm User inputs data via GUI/CLI
Data saved locally →
synchronized
C++ calls Python ML script
ML processes + predicts
C++ displays forecasts &
reports Workflow process

7. Fig: WORKFLOW DIAGRAM

8. Results and Findings Fig: Prediction Chart

9. Results and Findings Fig: Welcome page

10. Fitaura effectively integrates fitness monitoring
with machine learning-based forecasts, enabling
users to anticipate and adjust their health habits.
Its flexible modular design offers both graphical
and command-line interfaces for versatile use.
Recommendations:
To enhance the app, future work could involve
implementing more sophisticated prediction
techniques, incorporating additional health
indicators, developing mobile versions, and
adding features to increase user interaction.
Conclusion & Recommendations

11. References
Scikit-learn Developers. (2024). Scikit-learn: Machine learning in Python. scikit-
learn.org. https://scikit-learn.org/stable/
Blanchette, J., & Summerfield, M. (2008). C++ GUI programming with Qt 4
(2nd ed.). Prentice Hall.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and
TensorFlow (2nd ed.). O’Reilly Media.
Executing Python Scripts from Other Languages. (n.d.). Real Python.
https://realpython.com/run-python-scripts/
Stack Overflow. (n.d.). How to Call Python from C++ Using QProcess.
NCC Education Ltd. (2025). Data Science and Machine Learning Course Materials.
Qt Widgets Documentation. https://doc.qt.io/qt- 6/qtwidgets-index.html
Github link for project repository:
https://github.com/pandeysulav/SUMMER_PROJECT_CPP

12. Thank you For
Attention!