Stress-Predict Dataset

本研究旨在开发压力预测数据集，并对从经历各种诱发情绪状态的健康个体收集的生物生理数据进行描述性，统计和分类分析，评估常见的生物生理压力指标的相对敏感性和特异性，并为开发精确的压力监测设备提供垫脚石。在这项研究中，35名健康志愿者执行了三种不同的压力诱导任务 (即Stroop颜色单词测试，访谈会话和过度换气会话)，每个任务之间的基线/放松时间为60分钟。使用Empatica手表连续记录血容量脉搏 (BVP)，搏动间隔和心率，而使用基于PPG的呼吸率估计算法估算呼吸率。

This study aims to develop a stress prediction dataset, conduct descriptive, statistical and classification analyses on biosignal and physiological data collected from healthy volunteers experiencing various induced emotional states, evaluate the relative sensitivity and specificity of common physiological stress indicators, and lay a foundational stepping stone for the development of precise stress monitoring devices. In this study, 35 healthy volunteers completed three distinct stress-inducing tasks: the Stroop Color-Word Test, an interview session, and a hyperventilation session, with a 60-minute baseline/relaxation interval between each task. Blood volume pulse (BVP), interbeat interval, and heart rate were continuously recorded using an Empatica smartwatch, while respiratory rate was estimated using a PPG-based respiratory rate estimation algorithm.

搜集汇总

背景与挑战

背景概述

Stress-Predict Dataset是一个用于压力预测研究的生物生理数据集，包含35名健康志愿者在三种压力诱导任务下的多模态生理数据。该数据集由爱尔兰国立大学戈尔韦分校和伯明翰大学于2022年发布，旨在为开发精确的压力监测设备提供支持。