AdaBoost-Powered IoT System for Real-Time Pediatric Sleep Pattern Recognition

This research presents an Internet of Things (IoT) system that uses AdaBoost to recognize pediatric sleep patterns in real-time. For pediatric patients, the accuracy and flexibility of traditional sleep monitoring devices is often insufficient. Improve the accuracy of sleep pattern classification by combining several weak classifiers into a robust model using the AdaBoost algorithm, an ensemble learning approach. Complete sleep data from pediatric patients is collected by integrating many sensors into the IoT framework. These sensors include accelerometers and heart rate monitors. A central processing unit receives data in real time from these sensors, and the AdaBoost algorithm examines it to detect and classify different stages of sleep as well as any irregularities. To help better treat sleep problems in children, the system gives useful information and alerts when there is a change in their typical sleep patterns. A useful tool for pediatric sleep monitoring and management, the AdaBoost-powered IoT system considerably surpasses conventional approaches in terms of accuracy and responsiveness, according to the experimental findings.