<![CDATA[This study addresses the problem of undetected falls and hazardous incidents in household living rooms, especially for child and elderly users who are prone to slipping on wet and narrow surfaces. The study aimed to design and implement a smart living room safety monitoring system using ESP32 microcontrollers, PIR sensors, magnetic sensors, load cells with HX711, and MPU6050 connected to the Internet of Things to provide real-time notifications to caregivers via mobile apps. This methodology follows a prototype-based IoT engineering approach, starting with a literature review and needs analysis, followed by hardware-software design, prototyping, iterative testing, and final evaluation in a simulated living room environment for various fall scenarios. The experimental data consisted of PIR logs, weight changes, system response time, and environmental conditions, which were statistically analyzed to determine the system's accuracy, reliability, and responsiveness. The results showed that the prototype was able to detect suspicious movement patterns and falls with good accuracy and trigger local alarms and Telegram notifications within about 2–3 seconds, while still functioningin poor living room conditions. It can be concluded that the proposed system meets the research objectives ofa low-cost and privacy-preserving living room safety solution for smart homes, with future work directed at integrating machine learning-based fall detection and expanding communication options beyond WiFi to improve resilience in various residential environments.]]>
