INHOZ

• 

  our prototype

Inspiration The INTELLIGENT HOSPITALIZATION (INHOZ) project was driven by the need to modernize patient care through advanced technology. Traditional monitoring systems often fall short in real-time analysis and proactive response. Our goal was to address these gaps by integrating IoT and AI to provide a comprehensive, intelligent monitoring solution that enhances patient safety and optimizes hospital operations.

What It Does INHOZ revolutionizes patient care by:

Real-time Monitoring: Continuously tracks vital signs such as ECG, temperature, and pulse. AI-Driven Insights: Utilizes machine learning to analyze data and predict potential health issues. Automated Alerts: Generates immediate alerts for abnormal conditions or emergencies. Data Integration: Combines data from multiple sensors and sources for a holistic view of patient health. User-Friendly Interface: Provides healthcare professionals with a web-based dashboard for easy monitoring and management. How We Built It Conceptualization: Established project goals and defined technical requirements. System Architecture: Designed the overall system structure, including data collection, processing, and visualization. Sensor Integration: Connected sensors (ECG, temperature, pulse) to an ESP32 Dev Module and set up Firebase for data storage. Machine Learning: Developed and trained AI models, including YOLO V5 for surveillance and other models for health metrics. Web Interface: Built a web-based dashboard for real-time data visualization and interaction. Testing: Performed rigorous testing to ensure system accuracy and reliability. Challenges We Ran Into Data Accuracy: Ensuring precise and reliable sensor readings required extensive calibration and validation. Real-Time Processing: Handling real-time data streams and maintaining low latency posed technical challenges. Integration Issues: Seamlessly integrating various hardware and software components required significant troubleshooting. User Interface Design: Creating an intuitive interface that meets the needs of healthcare professionals involved ongoing feedback and adjustments. Accomplishments That We're Proud Of Successful Integration: Achieved seamless integration of multiple sensors and data sources. Effective AI Models: Developed machine learning models that accurately predict and detect health issues. User-Friendly Interface: Delivered a web-based dashboard that enhances usability for medical professionals. Recognition: Won two national hackathons, a project expo, and paper presentations using INHOZ. What We Learned Real-Time Data Handling: Gained experience in managing and analyzing continuous data streams. Machine Learning Implementation: Learned how to apply AI techniques effectively in a healthcare context. System Integration: Developed skills in integrating diverse hardware and software components. Healthcare Dynamics: Better understood the practical needs and challenges in hospital environments. What's Next for INHOZ Enhanced Features: Plan to add more advanced analytics and predictive capabilities. Wider Deployment: Aim to expand the system’s use to more hospitals and healthcare settings. Continuous Improvement: Focus on refining the system based on user feedback and emerging technologies. Collaborations: Explore partnerships with healthcare providers for further development and integration.

Built With

• azure-databases:-firebase-realtime-database
• c++
• css-frameworks:-tensorflow
• esp32-dev-module-cloud-services:-aws
• google-cloud
• html
• javascript
• languages:-python
• nosql-apis:-firebase-api
• pytorch
• sensor
• yolo-v5-platforms:-firebase