TinyML models enable on-device arrhythmia detection

Researchers have developed ArrythML, a TinyML approach for on-device arrhythmia detection using autoencoder models. These INT8 quantized models are designed for resource-constrained embedded systems, processing over 95,000 ECG segments on an ESP32-S3 microcontroller. The best-performing model achieved an 84% recall and 79% F1-score with a 180 KB size and 9 ms inference latency, demonstrating the potential for low-power, privacy-preserving wearable devices. AI

IMPACT Enables low-power, privacy-preserving wearable devices for real-time health monitoring.

RANK_REASON The cluster contains an academic paper detailing a new methodology and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• ArrythML
• autoencoder
• ECG
• ESP32-S3
• INT8
• MIT-BIH Arrhythmia Database
• TensorFlow Lite Micro
• TinyML

• paper
• product
• other

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