<![CDATA[The increasing risk of falls among the elderly necessitates the development of reliable monitoring systems for timely intervention. This study presents the design and development of an Internet of Things (IoT)-based activity monitoring system utilizing an ESP32-CAM, a PIR HC-SR501 sensor, and an Active Photoelectric Single Infrared Beam sensor, with data managed via a Firebase Realtime Database. The system’s logic employs an AND condition, whereby the camera is activated only upon the simultaneous detection of human presence by the PIR sensor and an interruption of the infrared beam exceeding five seconds, indicating a potential fall or abnormal event. Performance evaluation was conducted through sensor detection tests and an analysis of communication quality, assessed by Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), Packet Delivery Ratio (PDR), and packet loss metrics. The experimental results demonstrate that the PIR sensor consistently detects motion at distances of 1 to 4 meters with 100% accuracy, while the infrared beam sensor operates effectively up to a 4-meter range. All captured images and notifications were successfully transmitted to the Firebase database, achieving an average response time of 3.72 seconds, a PDR of 100%, and 0% packet loss. These findings confirm that the proposed system is a robust and reliable solution for monitoring elderly activities and delivering real-time notifications in indoor settings, thereby offering a viable tool for enhancing elderly safety and facilitating prompt family intervention.]]>
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