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What is TinyML for Real-Time Snoring Detection about?

This paper presents a TinyML-based health monitoring system for real-time snoring detection on resource-constrained wearable IoT devices, achieving 95.85% accuracy with low latency. The system utilizes advanced audio preprocessing and a compact model to operate effectively within the limitations of wearable technology. The device captures snoring sounds, alerts users, and logs data for further analysis, addressing the need for efficient snoring detection in healthcare.

Author

cn29765

Language

EN