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.
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Mrs K G Suhirdham
Srinithi A
Vishalini S
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Suhirdham et al. (Thu,) studied this question.
www.synapsesocial.com/papers/69d896406c1944d70ce07a0c — DOI: https://doi.org/10.5281/zenodo.19471347