A Dataset and Toolkit for Multiparameter Cardiovascular Physiology Sensing on Rings
Journal:
arXiv
Published Date:
May 7, 2025
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.
