Project Title: Android application: AI Smart Habit Tracker
Category: Machine Learning / AI
Project File: Download Project File
Anam Naveed
anam.naveed@vu.edu.pk
live:anam13dec
Project Domain / Category:
Artificial Intelligence/Machine learning
AI Smart Habit Tracker is a mobile application for users who want to maintain their healthy habits according to their needs and choice. The main goal of the project is to help users to establish and maintain positive habits like exercise, hydration, reading and other personal development. The app includes artificial intelligence (AI) to provide personalized suggests for tracks progress, habit- buildings and visualizes user data over time. Visual tools like progress bars, graphs, and calendars provide a snapshot of the user’s consistency over time. It helps to motivate users with reminder, rewards and social features in order to keep them engaged and on track towards their goals.
One other main objective of the project is to provide user-friendly platform that can track progress data easily. The app can also integrate with external fitness trackers for seamless tracking of physical activities.
By using AI for habit suggestions and providing real-time data visualization, the app aims to empower individuals to achieve their personal goals and lead healthier, more productive lives. Based on the data gathered over time, the app recommends new habits or adjustments to existing ones. The project will have significant potential in various markets, from health and wellness to corporate programs, making it a valuable tool for personal growth. This app is really helpful for individuals who are looking for personal growth, busy professional, students as well as Health and Fitness Enthusiasts.
1. The system shall able to allow users to sign up using email, social media accounts or phone number.
2. Users should have the ability to log in securely through their registered credentials.
3. Users shall able to reset password through if they forgot.
4. Users must be able to create and edit a personal profile, including basic information such as name, age, and preferences.
5. User can create multiple good habits (drinking water, exercising, reading any good book, offer prayer, reciting Quran etc.) at a time with details
6. Details of habits includes:
a. Habit Name
b. Goal setting(30 minute of exercise, drinking 2 liter water)
c. Frequency (i.e. daily, weekly, 5 times a day)
d. Start and end date of habits
e. Customizable reminder settings for habit completion.
7. User can delete and edit the habit details they have created.
8. User can maintain log for each habit on daily basis or according to frequency.
9. System shall provide data visualization tool to track the habit progress ( chart, graphs, progress bars etc.)
10. Users should be able to set up notifications to remind them to complete their habit at specific times or intervals
11. Users should receive real-time push notifications to encourage habit completion or remind them about pending tasks.
12. Users should earn points or virtual rewards for completing habit tasks and achieving milestones (e.g., "Completed 10 workouts").
13. The system should award badges for significant accomplishments (e.g., "30-Day Hydration Champion").
14. The system should analyze user behavior (habit logs, preferences, etc.) to recommend new habits or adjustments to existing ones based on performance and goals. Machine Learning (ML) models / Supervised Learning will analyze user behavior over time, such as the completion rates of specific habits, the frequency of habit attempts, and the times of day habits are performed. For instance, if a user consistently completes a specific habit (e.g., exercising every Monday at 7 AM), the system can learn this pattern. Recommendation Systems will use this data to suggest new habits or adjust existing ones based on the user’s preferences and performance.
15. Based on past performance, the system should suggest optimizations such as increasing or decreasing habit intensity (e.g., more frequent workouts or a longer reading time). RL can use to implement this feature. Reinforcement Learning (RL): This machine learning approach is used to dynamically adjust habit intensity based on user feedback (success or failure). The system will treat habit performance as an environment, and the user’s behavior will be the agent. If the user is performing well (completing the habit regularly), the system can gradually increase the habit’s intensity.
Android studio: Android Studio is the official IDE for Android development, backed by Google. It supports all the necessary tools for designing, building, testing, and debugging Android apps.
PyCharm: This is the most widely used IDE for Python. It offers excellent support for machine learning libraries.
Python: is the primary language used for AI and machine learning, making it the ideal backend language for implementing personalized habit suggestions and analyzing user behavior.
Database: PostgreSQL
Python:
https://www.python.org/ https://www.w3schools.com/python/ https://www.tutorialspoint.com/python/index.htm AI Techniques
https://www.ibm.com/think/topics/supervised-learning https://www.coursera.org/articles/types-of-machine-learning https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-ml/ https://towardsdatascience.com/top-10-algorithms-for-machine-learning-beginners- 149374935f3c https://www.nvidia.com/en-us/glossary/recommendation-system/
Supervisor:
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