Daily Step Tracker 🚶‍♂️ 👣 📈

The Daily Step Tracker is a web application that helps users track their daily step counts, visualize trends, and get AI-powered insights. It provides a simple and intuitive interface for uploading step count data, viewing historical trends, and generating predictions for future step counts.

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Features

• User Authentication: Users can sign up and log in to access their personalized step count data.
• Data Upload: Users can upload their daily step count data manually .
• Trend Analysis: Visualize daily step trends using interactive line graphs.
• Active vs. Inactive Days: Categorize days as active or inactive based on step count thresholds.
• Weekly/Monthly Averages: View average step counts per week or month.
• AI-Powered Insights:
  • Predict step counts for the next 7 days using linear regression.
  • Receive simple recommendations (e.g., "Increase activity on weekends").
• User-Specific Data: Each user’s data is stored separately, ensuring privacy and personalization.

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Technologies Used

Backend

• Flask: A lightweight Python web framework for building the backend API.
• SQLite: A lightweight database for storing user and step count data.
• Scikit-Learn: A machine learning library for generating step count predictions.

Frontend

• Streamlit: A Python library for building interactive web apps.
• Matplotlib: A plotting library for creating visualizations.
• Seaborn: A statistical data visualization library.

Deployment

• Render: A cloud platform for deploying the Flask backend.
• Streamlit Sharing: A platform for deploying the Streamlit frontend.

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How It Works

1. User Authentication:

   • Users sign up or log in using a username and password.
   • Each user is assigned a unique user_id to ensure data privacy.

2. Data Upload:

   • Users can upload their daily step count data manually .
   • The data is stored in a SQLite database.

3. Trend Analysis:

   • Users can view their daily step trends using interactive line graphs.
   • Days are categorized as active or inactive based on step count thresholds.

4. AI-Powered Insights:

   • The app predicts step counts for the next 7 days using linear regression.
   • Users receive simple recommendations to improve their activity levels.

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** Link to the site: https://daily-step-tracker-frontend-qteej6n4nuzpwbtkwfjzcv.streamlit.app/

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Screenshots

LoginPage

https://github.com/Adarshmohanp/DailyStepTracker/blob/main/screenshots/Login.png

UploadData

https://github.com/Adarshmohanp/DailyStepTracker/blob/main/screenshots/viewdata.png

ViewData

https://github.com/Adarshmohanp/DailyStepTracker/blob/main/screenshots/viewdata.png

Visualization

https://github.com/Adarshmohanp/DailyStepTracker/blob/main/screenshots/visualization1.png https://github.com/Adarshmohanp/DailyStepTracker/blob/main/screenshots/visualizatio2.png https://github.com/Adarshmohanp/DailyStepTracker/blob/main/screenshots/visualization3.png https://github.com/Adarshmohanp/DailyStepTracker/blob/main/screenshots/visualization4.png

Prediction

https://github.com/Adarshmohanp/DailyStepTracker/blob/main/screenshots/prediction1.png https://github.com/Adarshmohanp/DailyStepTracker/blob/main/screenshots/prediction2.png

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DemoVideo

Link: https://drive.google.com/file/d/1iY5sd46RUWiP7WVsESC7MC7aWF0W51Fs/view?usp=sharing

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