Smart Sleep Posture Monitoring System Using Embedded Sensors and IoT

Abstract

Sleep posture plays a critical role in determining spinal alignment, respiratory health, and overall sleep quality. This study proposes a smart sleep posture monitoring system that utilizes embedded sensors and Internet of Things (IoT) technologies to analyze and improve sleep behavior. The system integrates an Inertial Measurement Unit (IMU) sensor (MPU6050) and Force-Sensitive Resistors (FSRs) for detecting body orientation and pressure distribution. Data is processed via an ESP32 microcontroller and transmitted to a mobile application through Wi-Fi or Bluetooth. A machine learning model is employed to classify sleep postures, and vibration alerts are triggered for corrective feedback. Experimental results demonstrated high classification accuracy, indicating strong potential for applications in healthcare monitoring, ergonomic improvement, and early diagnosis of sleep disorders.