IoT-Based Sleep Quality Monitoring Simulation With Sleep Condition Analysis Using Fuzzy Logic

Abstract

This research aims to develop a low-cost IoT-based sleep quality Monitoring Simulation with Sleep Condition analysis using fuzzy logic for real-time physiological data acquisition and classification. The study employed the ADDIE development model (Analysis, Design, Development, Implementation, Evaluation). The system integrates MAX30100 heart rate sensor, DHT22 temperature sensor, and MPU6050 motion sensor with an ESP32 microcontroller. A Mamdani fuzzy inference system was implemented to classify Sleep Conditions based on heart rate, body temperature, and body movement parameters. The system was simulated using Wokwi and Cisco Packet Tracer, with fuzzy rules developed in MATLAB and embedded programming in Arduino IDE. The Blynk IoT platform was utilized for remote monitoring. Validation included Black Box Testing and User Acceptance Testing (UAT) with ten respondents. The system successfully acquired real-time physiological data and transmitted it to the Blynk cloud platform. The fuzzy logic algorithm effectively processed sensor data uncertainty, classifying health conditions into "Healthy," "Warning," or "Critical" status. Black Box Testing confirmed all main functions operated according to specifications. UAT yielded an 88% satisfaction score, indicating the system is highly feasible, user-friendly, and beneficial for independent sleep monitoring. This research contributes a practical, affordable IoT-based sleep monitoring architecture integrating fuzzy logic for Sleep Condition classification on embedded systems, offering an accessible alternative to conventional clinical methods for personal health management.