SnoozeMonitor

Your ultimate Sleep Comapnion

SnoozeMonitor is a comprehensive sleep monitoring and management application designed using Taipy, an open source Python library, to help users track, analyze, and improve their sleep quality. Whether you struggle with insomnia, sleep apnea, or simply want to optimize your sleep patterns, SnoozeMonitor offers personalized insights and recommendations to enhance your overall well-being.

Theme

The theme of SnoozeMonitor centers on promoting relaxation and well-being. Through its calming colors, smooth transitions, and user-friendly design, the app aims to create a serene environment that encourages better sleep habits. It emphasizes the importance of restful sleep for overall health and happiness.

Features

• Data Analysis: Analyze sleep-related data including gender distribution, disease counts by gender, sleep duration distribution by gender, stress level distribution by gender, and BMI distribution.
• Interactive Plots: Visualize data with interactive plots for better insights.
• Prediction: Predict sleep-related issues based on user input data.
• Precautionary Measures: Provide precautionary measures based on predicted sleep-related issues.

Demo

demo.mp4

Libaries

• Taipy 3.1.0 (an open-source Python library for easy, end-to-end application development)

  Taipy Components used in SnoozeMonitor

  • Taipy.Gui
  • Navbar
  • Input fields (text/number/sliders)
  • Buttons
  • Column Layout
  • Event Triggered Notifications
  • Visualization charts (Pie chart/Bar graphs/Histogram/Area chart/Box plots)
  • Images

• Tensorflow (Python framework to build ML/DL models)

• Scikit-learn (used to o implement machine learning models and statistical modelling)

• Open AI

• Plotly (Python graphing library makes interactive, publication-quality graphs)

Installation

For detailed installation instructions, please refer to the installation.md file.

Acknowledgements

• This project utilizes the Taipy framework for GUI development.
• Data analysis and visualization powered by Pandas, Plotly, and other Python libraries.
• Sleep disorder predictions are based on Neural Networks trained using TensorFlow.

Contributor:

Kamalakar Satapathi

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