Resumen
As medical technology continues to evolve, the importance of real-time feedback from physiological signals is increasingly being recognized. The advent of the Internet of Things (IoT) has facilitated seamless connectivity between sensors and virtual networks, enabling the integration of thoughtful medical care with real-time feedback capabilities. This project uses cloud storage technology and cloud software algorithms to enable data sharing and real-time feedback. Its main focus is to provide a system for real-time feedback on physiological signals and sleep quality analysis. The system uses smart wristbands and smart mobile devices to collect, transmit, and analyze physiological data. During sleep, users wear these devices, which capture and analyze their physiological data. The analyzed data are then stored in a cloud-based database. The research involves studying sleep quality and determining optimal sleep quality parameters based on the data stored in the cloud database. These parameters are designed to improve sleep quality. They are then transmitted to a mobile sleep aid device to control light conditions. The sleep aid software used in previous generations of mobile devices is the basis for expanding the integration of the sleep detection system. By combining the software of a mobile device platform with that of a smart wearable device, data can be obtained to monitor the wearer?s movements, such as turning over and heartbeat. The monitoring aspect includes tracking the turning time, distance, and speed, while the heartbeat monitoring includes detecting changes in heart rate, frequency, and interval using photoplethysmography (PPG) and smart wearable devices. Subsequently, artificial intelligence methods are employed to conduct statistical analysis and categorize the gathered extensive dataset. The system reads the data and provides the user with assessments and suggestions to improve sleep quality and overall sleep condition.