Literature survey demonstrates on-device AI and TinyML workflows for real-time fall detection using embedded motion sensors, highlighting low-power edge computing strategies.
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.
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Khan et al. (2026) studied this question.
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