Robust and Energy-efficient PPG-based Heart-Rate Monitoring

• URL: http://arxiv.org/abs/2203.16339v1
• Date: Mon, 28 Mar 2022 13:38:28 GMT
• Title: Robust and Energy-efficient PPG-based Heart-Rate Monitoring
• Authors: Matteo Risso, Alessio Burrello, Daniele Jahier Pagliari, Simone Benatti, Enrico Macii, Luca Benini, Massimo Poncino
• Abstract summary: wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU to enable non-invasive and comfortable monitoring. Recent state-of-the-art algorithms combine PPG and inertial signals to mitigate effect of motion artifacts. We propose the use of hardware-friendly Temporal Convolutional Networks (TCN) for PPG-based heart estimation.
• Score: 22.7371904884504
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: A wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU to enable non-invasive and comfortable monitoring, but ensuring robust PPG-based heart-rate monitoring in the presence of motion artifacts is still an open challenge. Recent state-of-the-art algorithms combine PPG and inertial signals to mitigate the effect of motion artifacts. However, these approaches suffer from limited generality. Moreover, their deployment on MCU-based edge nodes has not been investigated. In this work, we tackle both the aforementioned problems by proposing the use of hardware-friendly Temporal Convolutional Networks (TCN) for PPG-based heart estimation. Starting from a single "seed" TCN, we leverage an automatic Neural Architecture Search (NAS) approach to derive a rich family of models. Among them, we obtain a TCN that outperforms the previous state-of-the-art on the largest PPG dataset available (PPGDalia), achieving a Mean Absolute Error (MAE) of just 3.84 Beats Per Minute (BPM). Furthermore, we tested also a set of smaller yet still accurate (MAE of 5.64 - 6.29 BPM) networks that can be deployed on a commercial MCU (STM32L4) which require as few as 5k parameters and reach a latency of 17.1 ms consuming just 0.21 mJ per inference.

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