IoT based System for Sleep Apnea Detection and Disorder Measurement

Sleep apnea is a critical condition which is identified by sleep disturbances caused by interrupted breathing, often accompanied by low oxygen levels, fatigue, and cardiovascular complications. Polysomnography is the conventional method for diagnosing sleep apnea that is expensive since it requires supervision in a clinical setting, and is impractical for continuous home monitoring. In this paper we propose a continuous, real time Internet of Things (IoT) based health monitoring system designed to detect sleep apnea using a threshold detection method. This system utilizes different physiological sensors like MAX30102 to measure heart rate and blood oxygen saturation (SpO2), a respiratory effort sensor to monitor breathing patterns, a galvanic skin response (GSR) sensor to show stress levels and an accelerometer to detect sleeping posture. These sensors are connected to an ESP32 microcontroller, which processes the data from the sensors and transmits it via Wi Fi to the Thing Speak cloud platform. When the system encounters any sensor reading that exceeds the predefined threshold value, it automatically triggers alerts using cloud services. This method does not include complex AI models, is noninvasive, cost effective, customizable, and permits user driven monitoring. As a result it offers a promising solution for at home sleep apnea detection, which is not only accessible but also enables patients and healthcare providers to respond immediately to concerning health events.