<![CDATA[Falls are one of the major risks threatening the safety of the elderly, potentially causing serious injuries or even death. To mitigate this risk, this study developed an Internet of Things (IoT)-based fall detection system using the MPU-6500 sensor and ESP32 microcontroller. The system is designed to be portable and worn in the user's pocket, detecting abnormal motion patterns through the analysis of body acceleration and orientation data. The research methodology includes hardware and software requirements analysis, the design of a fall detection algorithm based on sum vector values and body orientation angles, and integration with a Telegram bot for real-time notifications. The system provides alerts through a wearable alarm, an external siren, and automated messages sent to caregivers or family members. Implementation results show that the system can detect falls accurately, respond quickly, and operate efficiently on low power, making it a practical and cost-effective solution to enhance elderly safety.]]>
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