Methodology

• The SMARTCARE system methodology is built around the design, development, and integration of four intelligent modules targeting key challenges in emergency healthcare. The development follows a hybrid lifecycle combining agile software engineering with data-driven machine learning workflows and IoT-based embedded hardware design. Each module—covering fall detection, AI-powered wound classification, hospital resource forecasting, and blood management—is developed independently to ensure modularity and scalability, before being integrated into a centralized healthcare support platform. This approach allows seamless interoperability and a unified real-time healthcare ecosystem.
• The SMARTCARE system methodology is built around the design, development, and integration of four intelligent modules targeting key challenges in emergency healthcare. The development follows a hybrid lifecycle combining agile software engineering with data-driven machine learning workflows and IoT-based embedded hardware design. Each module—covering fall detection, AI-powered wound classification, hospital resource forecasting, and blood management—is developed independently to ensure modularity and scalability, before being integrated into a centralized healthcare support platform. This approach allows seamless interoperability and a unified real-time healthcare ecosystem.
• Module-specific methodologies included the development of an ESP32-based wearable fall detection device using MPU6050 sensors, with fall detection algorithms filtering false positives and transmitting alerts with GPS location via Firebase to hospital dashboards and mobile apps. The wound classification module utilized a MobileNetV2 CNN trained on over 5,000 augmented wound images to provide severity grading and triage recommendations integrated into a mobile app. Medicine stock and ICU bed forecasting used LSTM models trained on a decade of hospital data to predict resource needs and enable proactive planning. The blood management system combined ARIMA and LSTM models for demand forecasting and real-time donor logistics using GPS and SMS/email alerts. Finally, all modules were integrated via a cloud backend ensuring real-time coordination, data privacy compliance, and robust testing with high accuracy and user acceptance scores.