使用Galaxy Watch在半自然环境下收集的PPG信号数据集。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Sangjun Park, Dejiang Zheng, Uichin Lee
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引用次数: 0

摘要

消费级可穿戴设备的广泛采用,如Galaxy Watch,已经彻底改变了个人健康监测,因为它们可以通过光电容积脉搏波(PPG)传感器连续和无创地测量关键的心血管指标。然而,现有的数据集主要依赖于研究级设备,限制了消费级可穿戴设备在现实环境中的适用性。为了解决这一差距,本研究提出了GalaxyPPG,这是一个从24名参与者收集的数据集,其中包括来自Galaxy Watch 5和Empatica E4的手腕上的PPG信号,以及来自Polar H10的胸前心电图数据。数据是在半自然环境下的各种活动中捕获的,可以深入了解消费级可穿戴设备在运动或压力诱导活动下的传感性能。该数据集旨在推进PPG信号的应用,例如各种体育活动的HR跟踪和用于应力检测的HRV监测。此外,我们还提供了一个开源工具包,用于使用三星Galaxy Watch进行数据收集和分析,以促进利用该工具包的可重复性和进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A PPG Signal Dataset Collected in Semi-Naturalistic Settings Using Galaxy Watch.

The widespread adoption of consumer-grade wearable devices, such as Galaxy Watch, has revolutionized personal health monitoring as they enable continuous and non-invasive measurement of key cardiovascular indicators through photoplethysmography (PPG) sensors. However, existing datasets primarily rely on research-grade devices, limiting the applicability of consumer-grade wearables in real-world conditions. To address this gap, this study presents GalaxyPPG, a dataset collected from 24 participants that includes wrist-worn PPG signals from a Galaxy Watch 5 and an Empatica E4, alongside chest-worn ECG data from a Polar H10. Data were captured during diverse activities in a semi-naturalistic setting, providing insights into the sensing performance of consumer-grade wearables under motion- or stress-inducing activities. This dataset is designed to advance applications of PPG signals, such as HR tracking with diverse physical activities and HRV monitoring for stress detection. Additionally, we offer an open-source toolkit for data collection and analysis using Samsung Galaxy Watch, fostering reproducibility and further research leveraging this toolkit.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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