Design de bracelets intelligents pour le contrôle des personnes âgées en utilisant l’intelligence artificielle

Abstract

This thesis presents a comprehensive IoT system that integrates artificial intelligence (AI) and smart wristbands to provide early detection of health anomalies in elderly individuals. The system, designed to monitor vital health parameters such as ECG signals, oxygen saturation, and heart rate, aims to empower elderly individuals to monitor their health continuously and receive timely alerts, thereby reducing the risks associated with delayed medical interventions. The system also provides real-time access to patient data and predictive insights for healthcare providers, enabling more informed decision-making and proactive care. The IoT system, comprising a mobile application, sensors, NRF module, BLE, Raspberry Pi, MQTT broker, and MongoDB database, integrates sensors to transmit data in real-time. Machine learning models deployed using Azure ML enable accurate anomaly detection, specifically focusing on sepsis, arrhythmia, and fall prediction. The findings of this thesis demonstrate the effectiveness of the system in enhancing patient safety and health outcomes, while reducing the burden on healthcare providers by facilitating remote monitoring and early detection of potential health issues. The system’s ability to provide timely alerts and predictive insights empowers elderly individuals to take proactive measures to manage their health, ultimately improving their quality of life.