Elderly Fall Detection System with ESP32 Module and Edge Impulse Studio

Falls are a common occurrence, particularly among the elderly, often leading to unintentional injuries and accidents that can have severe consequences, necessitating swift intervention. Individuals living alone frequently face delays in obtaining the essential emergency medical assistance they require during such incidents. Consequently, accurately detecting falls and promptly initiating emergency aid is of paramount importance. In this work, a fall detection system was developed using machine learning algorithms, utilizing data collected from the MPU6050 IMU sensor through the Edge-Impulse platform. This system automatically identifies when a person experiences a fall and promptly notifies the user or relevant individuals. The primary goal of this project is to enhance the safety of elderly and injured individuals by enabling rapid intervention during fall incidents. The system leverages Edge Impulse to streamline the development of machine learning algorithms. A dataset containing accelerometer signal recordings of five normal activities and four falling cases is constructed. A sequential neural network model was trained and accuracy of 88.29% on the test set was obtained. Further enhancements in the accuracy of the real-time system can be achieved by increasing the size of the dataset.