Physiological signals during activities for daily life: Dataset

The dataset used in this work is composed by four participants, two men and two women. Each of them carried the wearable device Empatica E4 for a total number of 15 days. They carried the wearable during the day, and during the nights we asked participants to charge and load the data into an external memory unit. During these days, participants were asked to answer EMA questionnaires which are used to label our data. However, some participants could not complete the full experiment or some days were discarded due to data corruption. Specific demographic information, total sampling days and total number of EMA answers can be found in table I. Participant 1 Participant 2 Participant 3 Participant 4 Age 67 55 60 63 Gender Male Female Male Female Final Valid Days 9 15 12 13 Total EMAs 42 57 64 46 Table I. Summary of participants' collected data. This dataset provides three different type of labels. <em>Activeness</em> and <em>happiness</em> are two of these labels. These are the answers to EMA questionnaires that participants reported during their daily activities. These labels are numbers between <em>0</em> and <em>4</em>.<br> These labels are used to interpolate the mental well-being state according to [1] We report in our dataset a total number of eight emotional states: (1) pleasure, (2) excitement, (3) arousal, (4) distress, (5) misery, (6) depression, (7) sleepiness, and (8) contentment. The data we provide in this repository consist of two type of files: <strong>CSV files</strong>: These files contain physiological signals recorded during the data collection process. The first line of each CSV file defines the timestamp by which data started being sampled. The second line defines the sampling frequency used for gathering the signal. From the third line until the end of the file, one can find sampled datapoints. <br> <strong>Excel files</strong>: These files contain the labels obtained from EMA answers. It is indicated the timestamp at which the answer was registered. Labels for <em>pleasure</em>, <em>activeness</em> and <em>mood</em> can be found in this file. <strong>NOTE: </strong>Files are numbered according to each specific sampling day. For example, ACC1.csv corresponds to the signal ACC for sampling day 1. The same applied to excel files. Code and a tutorial of how to labelled and extract features can be found in this repository: https://github.com/edugm94/temporal-feat-emotion-prediction References: [1] . A. Russell, “A circumplex model of affect,” Journal of personality and social psychology, vol. 39, no. 6, p. 1161, 1980本研究所使用的数据集共招募4名受试者，其中男性2名、女性2名。所有受试者均佩戴Empatica E4可穿戴设备（Empatica E4），佩戴时长总计15天。受试者日间需持续佩戴设备，夜间则需将设备充电并将数据导入外部存储单元。 试验期间，受试者需填写EMA问卷（EMA）以完成数据标注。但部分受试者未能完成全部试验，部分日期因数据损坏被剔除。受试者的具体人口统计学信息、总有效采样天数及EMA问卷总作答数详见表1。 表1 受试者采集数据汇总 | 受试者编号 | 年龄 | 性别 | 最终有效天数 | 总EMA作答数 | | :---: | :---: | :---: | :---: | :---: | | 1 | 67 | 男 | 9 | 42 | | 2 | 55 | 女 | 15 | 57 | | 3 | 60 | 男 | 12 | 64 | | 4 | 63 | 女 | 13 | 46 | 本数据集包含三类不同的标注标签，其中**活跃度（Activeness）**与**愉悦度（happiness）**为其中两类标签，二者均来自受试者日常活动期间填写的EMA问卷作答结果，取值范围为0至4的整数。 基于上述标签，可参照文献[1]的方法插值得到受试者的心理健康状态。本数据集共涵盖8种情绪状态：(1) 愉悦（pleasure）、(2) 兴奋（excitement）、(3) 唤醒（arousal）、(4) 苦恼（distress）、(5) 痛苦（misery）、(6) 抑郁（depression）、(7) 嗜睡（sleepiness）及(8) 满足（contentment）。 本仓库提供的数据包含两类文件： 1. **"CSV文件"**：此类文件存储数据采集过程中记录的生理信号。每个CSV文件的首行定义数据开始采样的时间戳，第二行定义信号采集的采样频率，从第三行至文件末尾则为采样得到的原始数据点。 2. **"Excel文件"**：此类文件存储通过EMA问卷作答得到的标注标签，文件中会记录作答提交的时间戳，其中包含愉悦（pleasure）、活跃度（activeness）及情绪（mood）三类标签。 **注**：文件编号与对应采样日期一一对应。例如，`ACC1.csv`对应采样第1天的加速度（ACC）信号，Excel文件的命名规则与此一致。 本仓库中同时提供了标注代码及特征提取教程，链接为：https://github.com/edugm94/temporal-feat-emotion-prediction 参考文献： [1] A. Russell, "A circumplex model of affect," *Journal of Personality and Social Psychology*, vol. 39, no. 6, p. 1161, 1980.