Inspiration
The idea was born out of the need to provide an easy way for individuals to monitor their BP and glucose levels, addressing the rising concerns of chronic health conditions like hypertension and diabetes. Early detection plays a critical role in preventing severe health complications.
What it does
The BP & Glucose Monitor app allows users to track their blood pressure and glucose levels in real time. The app syncs with Bluetooth-enabled sensors, stores data securely, and provides insights like trends and alerts for abnormal readings, ensuring timely intervention.
How we built it
The project was developed using React Native for cross-platform mobile development. It integrates with Bluetooth-enabled BP and glucose sensors to collect real-time health data. The backend, built with Node.js and MySQL, handles data storage, trend analysis, and user management.
Challenges we ran into
- Hardware Integration: Ensuring the BP and glucose sensors worked seamlessly with the app was difficult.
- Data Accuracy: Sensor readings often had inconsistencies that needed to be calibrated.
- User Interface Design: Designing a simple yet informative interface that would be easy for users to navigate, especially for those with health conditions.
- Real-Time Syncing: Making sure data was transmitted and synced accurately in real time was a technical challenge.
Accomplishments that we're proud of
- Successfully integrated Bluetooth sensors with the mobile app for real-time data collection.
- Developed a clean and intuitive interface that makes health tracking accessible to users of all tech levels.
- Implemented trend analysis and real-time alerts to help users manage their health better.
What we learned
- The importance of sensor calibration and ensuring data accuracy.
- How to develop a cross-platform mobile app that syncs seamlessly with external hardware.
- The role of real-time data collection and analysis in healthcare technology.
What's next for BP & Glucose Monitor: Early Detection, Better Protection
Future improvements include expanding sensor compatibility, adding AI-driven insights for health predictions, and enhancing the app's usability with features like voice commands and more personalized health recommendations.
Built With
- api
- arduino
- c++
- cgm)
- esp32
- ide
- javascript
- jetson-nano
- lite
- matplotlib
- native
- node.js
- numpy
- pandas
- photoplethysmography
- postgresql
- ppg)
- react
- rechargablecharger
- rest
- scikit-learn
- tensorflow
- wearablecase

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