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Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devicesusing low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts(MAs) caused by movements of the subject's arm affect the performance ofPPG-based HR tracking. This is typically addressed coupling the PPG signal withacceleration measurements from an inertial sensor. Unfortunately, most standardapproaches of this kind rely on hand-tuned parameters, which impair theirgeneralization capabilities and their applicability to real data in the field.In contrast, methods based on deep learning, despite their bettergeneralization, are considered to be too complex to deploy on wearable devices.  In this work, we tackle these limitations, proposing a design spaceexploration methodology to automatically generate a rich family of deepTemporal Convolutional Networks (TCNs) for HR monitoring, all derived from asingle "seed" model. Our flow involves a cascade of two Neural ArchitectureSearch (NAS) tools and a hardware-friendly quantizer, whose combination yieldsboth highly accurate and extremely lightweight models. When tested on thePPG-Dalia dataset, our most accurate model sets a new state-of-the-art in MeanAbsolute Error. Furthermore, we deploy our TCNs on an embedded platformfeaturing a STM32WB55 microcontroller, demonstrating their suitability forreal-time execution. Our most accurate quantized network achieves 4.41 BeatsPer Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of47.65 mJ and a memory footprint of 412 kB. At the same time, the smallestnetwork that obtains a MAE < 8 BPM, among those generated by our flow, has amemory footprint of 1.9 kB and consumes just 1.79 mJ per inference.

Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices