Stress analysis from physiological data under pressure: WorkStress3D Dataset

This phrase describes a research approach that analyzes physiological signals to gain insights into psychological stress.

The dataset includes survey responses from 20 participants. In this data type, 4 different questionnaires were used: demographic information, instant questionnaires, panas scale and general stress test.

Facial expression:
The facial expressions in this file are classified according to 6 emotions. In addition, this dataset is categorized as binary for the stress state.
The datasets in this file contain 48 x 48 pixel data in grayscale.

AudioData file: raw audio data

Physiological Signal: The physiological signals of participants were sampled at varying frequencies using the wrist-worn Empatica E4 sensor device. To resolve this issue, a downsampling technique was used to make the frequencies of the signals more uniform. Downsampling was chosen to achieve a balance between computing efficiency and accuracy. By downsampling all physiological inputs to 4 Hz, consistency and effective fusion were attained.

本表述阐述了一种通过分析生理信号以探析心理压力机制的研究方法。

该数据集涵盖20名参与者的问卷应答数据。本数据集采用了四类不同问卷：人口统计学信息问卷、即时问卷、积极与消极情感量表（PANAS Scale）以及一般压力测试问卷。

面部表情模块：
本文件中的面部表情数据依据6种情感类别进行标注分类。此外，该数据集针对压力状态设置了二分类标签。本文件内的数据集包含分辨率为48×48像素的灰度图像数据。

音频数据文件：原始音频数据

生理信号：参与者的生理信号由腕戴式Empatica E4传感器设备以差异化采样频率进行采集。为解决采样频率不统一的问题，研究采用了下采样技术以统一各信号的采样频率。选择下采样方案旨在兼顾计算效率与识别准确率。通过将所有生理输入信号下采样至4Hz，实现了信号的一致性与有效融合。