PPGWeaver: Diffusion-Augmented Models for Real-Time Heart Rate Estimation on Microcontrollers

Le résumé fourni par la source

We present a compact, deployable heart rate (HR) estimation system using photoplethysmography (PPG) and inertial measurement unit (IMU) data, combining TimeWeaver, a conditional diffusion model for metadata-aware synthetic augmentation, with progressive structured pruning of Temporal Convolutional Networks (TCNs). Our smallest model, with 1.56k parameters, achieves a mean absolute error (MAE) of 4.92 BPM on the PPG-DaLiA dataset and supports real-time inference ($<{40 ms}$latency) on a 64 MHz ARM Cortex-M4F microcontroller (MCU) without requiring quantization. Synthetic data conditioned on subject metadata, HR, and activity type significantly enhances model generalization, enabling pruned models to match or exceed the accuracy of larger baselines, achieving over a 23% improvement compared to training on real data alone. Our work establishes a new Pareto frontier for real-time, on-device HR monitoring using diffusion-augmented training and sub-2 k parameter models.

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