The implementation of the Smart Elderly Fall Detection System begins by identifying the system requirements and selecting suitable hardware components such as an ESP32 or ESP8266 NodeMCU, an MPU6050 accelerometer and gyroscope sensor, a GPS module for location tracking, and a buzzer for local emergency alerts. The development environment is configured using the Arduino IDE for embedded programming and Python with Flask for backend application development. A MySQL or Firebase database is created to store user information, sensor readings, and emergency event records.
The MPU6050 sensor is connected to the IoT controller to continuously monitor acceleration and body orientation. Embedded software written in C/C++ collects motion data at regular intervals and applies a fall detection algorithm that identifies sudden acceleration changes followed by inactivity, which typically indicate a fall. If required, GPS coordinates are also collected to determine the user's location. Once a fall is detected, the controller activates the local buzzer and transmits the emergency information to the cloud server using MQTT or HTTP over a Wi-Fi connection.
A backend application is developed using Python and Flask to receive sensor data, validate emergency events, and store incident records in the database. REST APIs are implemented to enable secure communication between the IoT device, cloud platform, and web application. Authentication mechanisms ensure that only authorised caregivers and administrators can access patient information and emergency alerts.
A responsive web dashboard is developed using HTML, CSS, and JavaScript to provide real-time monitoring of elderly users. The dashboard displays live device status, recent fall incidents, GPS location, emergency notifications, and historical event records. Caregivers can acknowledge alerts, review incident history, and monitor multiple users through a single interface. Graphical charts are included to visualise motion data and system activity over time.
Finally, the complete system is tested under various movement scenarios to verify the accuracy of the fall detection algorithm and minimise false alarms. Communication reliability, sensor calibration, dashboard performance, and emergency notification delivery are thoroughly evaluated. After successful testing, the application is deployed on a local or cloud server, enabling continuous remote monitoring of elderly individuals and ensuring timely assistance during emergency situations.
