论文标题
MDEAW：通过EDA和PPG信号的多模式数据集，来自无线可穿戴式低成本设备的PPG信号
MDEAW: A Multimodal Dataset for Emotion Analysis through EDA and PPG signals from wireless wearable low-cost off-the-shelf Devices
论文作者
论文摘要
我们提出了MDEAW，这是一个由电甲型活动（EDA）和光插图学（PPG）组成的多模式数据库（PPG）信号，在考试期间记录了由巴塞罗那Sabadell eurecat Academy教师教授的课程，以引起对教室场景中的学生的情感反应。以6种基本的情感状态来看，每次刺激后，都记录了10名学生的信号，以及学生对自己情感状态的自我评估。所有信号均使用便携式，可穿戴，无线，低成本和现成的设备捕获，该设备有可能在日常应用中使用情感计算方法。使用基于EDA和PPG的功能及其融合的基线是通过remecs，fed-emecs和fed-emecs-u建立的。这些结果表明，使用低成本设备进行情感状态识别应用的前景。提出的数据库将公开可用，以便研究人员对这些捕获设备对情绪状态识别应用的适用性进行更彻底的评估。
We present MDEAW, a multimodal database consisting of Electrodermal Activity (EDA) and Photoplethysmography (PPG) signals recorded during the exams for the course taught by the teacher at Eurecat Academy, Sabadell, Barcelona in order to elicit the emotional reactions to the students in a classroom scenario. Signals from 10 students were recorded along with the students' self-assessment of their affective state after each stimulus, in terms of 6 basic emotion states. All the signals were captured using portable, wearable, wireless, low-cost, and off-the-shelf equipment that has the potential to allow the use of affective computing methods in everyday applications. A baseline for student-wise affect recognition using EDA and PPG-based features, as well as their fusion, was established through ReMECS, Fed-ReMECS, and Fed-ReMECS-U. These results indicate the prospects of using low-cost devices for affective state recognition applications. The proposed database will be made publicly available in order to allow researchers to achieve a more thorough evaluation of the suitability of these capturing devices for emotion state recognition applications.
