MDEAW: A Multimodal Dataset for Emotion Analysis through EDA and PPG signals from wireless wearable low-cost off-the-shelf Devices. (arXiv:2207.06410v1 [cs.HC])

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



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