The paper aims to create an affordable wearable fall detection system aimed at accurately identifying falls. The system comprises a smart armband prototype equipped with embedded sensors to capture motion data from elderly individuals during their daily activities. The primary objectives include designing a compact hardware prototype incorporating an accelerometer, and gyroscope, with a microcontroller unity, implementing fall detection algorithms utilizing threshold-based techniques and machine learning models for real-time classification of falls versus other activities, and optimizing the algorithms for on-device execution with limited computing resources. The ultimate goal is to develop a non-invasive, cost-effective solution for elderly fall monitoring that is both accurate and robust. The prototype’s development will encompass hardware design and optimization of the fall detection algorithm for on-device inference, with future research potentially integrating this system into assistive technologies to enhance safety for independently living elderly individuals.