A Literature Survey on Implementation of On-Device AI Algorithms for Fall Detection Using Embedded Motion Sensor Data

Falls among elderly individuals remain one of the major causes of accidental injury and hospitalization worldwide. Recent advancements in Embedded Artificial Intelligence (AI), TinyML, wearable sensing, and edge computing have enabled intelligent fall detection systems capable of operating directly on low-power embedded processors. This paper presents a detailed literature survey of AI-driven embedded fall detection systems focusing on motion sensors, sound analysis, multimodal fusion, TinyML deployment, model optimisation, and communication alert mechanisms. The paper additionally analyses the complete Edge AI workflow involving sensor data acquisition, preprocessing, AI model training, model optimisation, embedded deployment, real-time inference, and emergency.