PuLsE: Accurate and Robust Ultrasound-Based Continuous Heart-Rate Monitoring on a Wrist-Worn IoT Device

This work explores the feasibility of employing ultrasound (US) technology in a wrist-worn Internet of Things (IoT) device for low-power, high-fidelity heart rate (HR) extraction. US offers deep tissue penetration and can monitor pulsatile arterial blood flow in large vessels and the surrounding tissue, potentially improving robustness and accuracy compared to photoplethysmogram (PPG). We present an IoT wearable system prototype utilizing a commercial microcontroller (MCU) employing the onboard analog-digital converters (ADC) to capture high-frequency US signals and an innovative low-power US pulser. An envelope filter lowers the bandwidth of the US signal by a factor of $\gt {\mathrm {5~x}}$ , reducing the system’s acquisition requirements without compromising accuracy (correlation coefficient between HR extracted from enveloped and raw signals, r(92) ${=} {\mathrm {0.996~}}$ , p $\lt {\mathrm {0.001~}}$ ). The full signal processing pipeline is ported to fixed-point arithmetic for increased energy efficiency and runs entirely onboard. The extracted HR can be transmitted to the cloud via a bluetooth low energy (BLE) module. The system has an average power consumption of 5.8 mW, competitive with commercial PPG-based systems, and the HR extraction algorithm requires only 69 kB of RAM and 71 ms of processing time on an ARM Cortex-M4-based MCU. The system is estimated to run continuously on a smartwatch battery for more than 7 days. To accurately evaluate the proposed circuit and algorithm and identify the anatomical location on the wrist with the highest accuracy for HR extraction, we collected a dataset from 10 healthy adults at three different wrist positions. The dataset comprises roughly 5 hours of HR data with an average of ${\mathrm {80.6~}}~\pm ~{\mathrm {16.3~bpm}}$ . During recording, we synchronized the established electrocardiography (ECG) gold standard with our US-based method. The comparisons yield a Pearson correlation coefficient of r(92) ${=} {\mathrm {0.99~}}$ , p $\lt {\mathrm {0.001~}}$ and a mean error of ${\mathrm {0.68~}}~\pm ~{\mathrm {1.88~bpm}}$ in the lateral wrist position near the radial artery. Moreover, we tested our method while walking and running to assess its robustness to motion artifacts, achieving a heart rate extraction accuracy of ${\mathrm {1.99~}}~\pm ~{\mathrm {2.80~bpm}}$ . The collected dataset and code used in this work have been open-sourced and are available at https://github.com/mgiordy/Ultrasound-Heart-Rate.