Smart IoT-Based Mobile App for Continuous Patient Monitoring

The Internet of Things (IoT) has emerged as a transformative force in healthcare by enabling continuous, real-time patient monitoring and data-driven clinical decision-making. This study introduces an IoT-based mobile application designed for the remote monitoring of critical physiological parameters—heart rate, body temperature, and blood oxygen saturation (SpO2)—using a designed low-cost wearable that connects the MAX30102 and MAX30205 sensors. Data are transmitted over WiFi via an MQTT protocol to a Flutter-based mobile platform to be visualized and analyzed. A predictive AI model (random forest classifier) was developed and integrated in the app that performs real-time anomaly detection and early disease prediction. Alerts generated through Google Cloud Messaging facilitate prompt clinical intervention. The proposed scalable system reduces the need for manual data collection while enhancing patient care and overall operational efficiency. Enabling remote monitoring, the proposed system provides patients with the opportunity to stay at home and engage in work and daily activities, particularly beneficial for those with chronic diseases. Future adaptations may involve collaboration with hospital clinicians to enhance the quality of life and promote independent living for patients.