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Inspiration
Sleep-related issues have become increasingly common due to excessive screen time, stress, irregular schedules, and unhealthy lifestyle habits. I wanted to create a simple and accessible solution that helps people understand their sleep patterns and encourages healthier sleep habits through personalized insights.
What it does
SleepSense AI is a web application that analyzes a user's sleep habits based on five key factors:
- Sleep Duration
- Bedtime
- Screen Time Before Bed
- Stress Level
- Caffeine Intake
Based on these inputs, the application generates a personalized sleep score, categorizes sleep quality, and provides actionable recommendations to improve sleep wellness.
How we built it
The application was built using:
- Next.js for the frontend
- Tailwind CSS for responsive UI design
- Flask for backend API development
- Python for sleep score calculation and recommendation logic
- GitHub for version control
- Vercel for deployment
The frontend collects user inputs and sends them to the Flask backend, which evaluates sleep-related factors and returns a sleep score along with personalized suggestions.
Challenges we ran into
- Integrating the frontend and backend smoothly.
- Handling API communication between Next.js and Flask.
- Managing deployment and repository issues caused by large generated files.
- Designing a simple scoring system that produces meaningful recommendations while keeping the user experience intuitive.
Accomplishments that we're proud of
- Successfully developed and deployed a full-stack wellness application.
- Created a clean and responsive user interface.
- Implemented personalized sleep analysis using lifestyle factors.
- Learned and applied Git, GitHub, deployment workflows, and API integration in a real project.
What we learned
Through this project, we gained practical experience in:
- Full-stack web development
- REST API integration
- Frontend and backend communication
- Version control with Git and GitHub
- Cloud deployment using Vercel
- Building user-centric health and wellness applications
What's next for SleepSenseAI
Future improvements include:
- Machine learning-based sleep quality prediction
- Sleep history tracking and analytics
- User authentication and personalized dashboards
- Smart reminders and bedtime notifications
- AI-powered sleep coaching and wellness insights
- Mobile application support
SleepSense AI aims to become a simple and effective digital companion that helps users build healthier sleep habits and improve their overall well-being.
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