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TinyML models enable on-device arrhythmia detection

By PulseAugur Editorial · [2 sources] ·

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 systems. AI

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

RANK_REASON This is a research paper detailing a novel approach to TinyML for on-device arrhythmia detection.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Nagarajan S, Kurian Polachan ·

    ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems

    arXiv:2606.02256v1 Announce Type: new Abstract: Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems. We develop INT8 quantized autoencoder-…

  2. arXiv cs.LG TIER_1 English(EN) · Kurian Polachan ·

    ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems

    Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems. We develop INT8 quantized autoencoder-based TinyML models with minimal layers and para…

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