A Dataset and Toolkit for Multiparameter Cardiovascular Physiology Sensing on Rings

TL;DR

This paper introduces $ au$-Ring, an open-source dataset and toolkit for cardiovascular sensing using smart rings, enabling improved estimation of vital signs through diverse data and analysis methods.

Contribution

It provides the first comprehensive, publicly available dataset and analysis toolkit for ring-based cardiovascular physiological sensing, covering multiple signals and activities.

Findings

Deep learning methods outperform physics-based approaches in vital sign estimation.

Achieved low MAE values for heart rate, respiratory rate, oxygen saturation, and blood pressure.

Dataset includes diverse activities and physiological states, supporting robust model development.

Abstract

Smart rings offer a convenient way to continuously and unobtrusively monitor cardiovascular physiological signals. However, a gap remains between the ring hardware and reliable methods for estimating cardiovascular parameters, partly due to the lack of publicly available datasets and standardized analysis tools. In this work, we present $\tau$-Ring, the first open-source ring-based dataset designed for cardiovascular physiological sensing. The dataset comprises photoplethysmography signals (infrared and red channels) and 3-axis accelerometer data collected from two rings (reflective and transmissive optical paths), with 28.21 hours of raw data from 34 subjects across seven activities. $\tau$-Ring encompasses both stationary and motion scenarios, as well as stimulus-evoked abnormal physiological states, annotated with four ground-truth labels: heart rate, respiratory rate, oxygen saturation, and blood pressure. Using our proposed RingTool toolkit, we evaluated three widely-used physics-based methods and four cutting-edge deep learning approaches. Our results show superior performance compared to commercial rings, achieving best MAE values of 5.18 BPM for heart rate, 2.98 BPM for respiratory rate, 3.22\% for oxygen saturation, and 13.33/7.56 mmHg for systolic/diastolic blood pressure estimation. The open-sourced dataset and toolkit aim to foster further research and community-driven advances in ring-based cardiovascular health sensing.