AI-Acoustic Guard: Real-Time Human Fall Detection Using TinyML And Edge Computing

Falls are a leading cause of injury, particularly among elderly individuals and vulnerable populations, necessitating efficient and real-time monitoring solutions. This paper presents AI-Acoustic Guard, a novel human fall detection system leveraging TinyML and edge computing for low-latency, privacy-preserving operation. Unlike conventional vision-based approaches, the proposed system utilizes acoustic signals captured through low-power microphones to identify characteristic sound patterns associated with human falls. A lightweight deep learning model is trained and optimized using TinyML techniques, enabling deployment on resource-constrained edge devices such as microcontrollers. The system processes audio data locally, eliminating the need for continuous cloud connectivity and ensuring data security. Feature extraction methods, including Mel-frequency cepstral coefficients (MFCCs), are employed to enhance classification accuracy. Experimental results demonstrate high detection accuracy, low false alarm rates, and minimal power consumption. The proposed approach is cost-effective, scalable, and suitable for real-world applications such as smart homes, assisted living facilities, and healthcare monitoring systems, offering a reliable solution for timely fall detection and emergency response.