Key result
Wrist-worn ultrasound device matches gold-standard ECG heart rate with a ~0.68 bpm mean error.
Why the study?
Ultrasound technology offers deep tissue penetration and arterial blood flow monitoring, potentially improving heart-rate extraction robustness and accuracy over photoplethysmography in wearable devices.
Does an ultrasound-based wrist-worn IoT device accurately measure heart rate compared to ECG in healthy adults?
Does an ultrasound-based wrist-worn IoT device accurately measure heart rate compared to ECG in healthy adults?
A novel low-power ultrasound-based wrist-worn IoT device provides highly accurate continuous heart rate monitoring comparable to ECG, even during motion.
Supports feasibility of motion-robust wrist ultrasound HR monitoring in healthy adults; leaves open validation in larger clinical cohorts.
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 analogdigital 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 >5 x, reducing the systems acquisition requirements without compromising accuracy (correlation coefficient between HR extracted from enveloped and raw signals, r(92)=0.996, p<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 5.8mW, 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 80.6116.3 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)=0.99, p<0.001 and a mean error of 0.6811.88 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 1.9912.80 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.
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Giordano et al. (2025) studied Healthy adults (n=10). Ultrasound-based continuous heart-rate monitoring device vs. Electrocardiography (ECG) was evaluated on Heart rate extraction accuracy compared to ECG (r=0.99, p=<0.001). A wrist-worn ultrasound-based IoT device accurately extracted heart rate compared to gold-standard ECG, achieving a Pearson correlation of r=0.99 (p<0.001) and a mean error of 0.68±1.88 bpm.
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