StressID: a Multimodal Dataset for Stress Identification

Hava Chaptoukaev, Valeriya Strizhkova, Michele Panariello, Bianca Dalpaos, Aglind Reka, Valeria Manera, Susanne Thummler, Esma ISMAILOVA, Nicholas Evans, Francois Bremond, Massimiliano Todisco, Maria A Zuluaga, Laura M. Ferrari

Keywords: Stress identification, dataset

TL;DR: StressID is the a novel and large dataset for stress identification with three different sources of data and three classes of stimuli.

Abstract:

StressID is a new dataset specifically designed for stress identification from
unimodal and multimodal data. It contains videos of facial expressions, audio
recordings, and physiological signals. The video and audio recordings are acquired
using an RGB camera with an integrated microphone. The physiological data
is composed of electrocardiography (ECG), electrodermal activity (EDA), and
respiration signals that are recorded and monitored using a wearable device. This
experimental setup ensures a synchronized and high-quality multimodal data col-
lection. Different stress-inducing stimuli, such as emotional video clips, cognitive
tasks including mathematical or comprehension exercises, and public speaking
scenarios, are designed to trigger a diverse range of emotional responses. The
final dataset consists of recordings from 65 participants who performed 11 tasks,
as well as their ratings of perceived relaxation, stress, arousal, and valence levels.
StressID is one of the largest datasets for stress identification that features three
different sources of data and varied classes of stimuli, representing more than
39 hours of annotated data in total. StressID offers baseline models for stress
classification including a cleaning, feature extraction, and classification phase for
each modality. Additionally, we provide multimodal predictive models combining
video, audio, and physiological inputs. The data and the code for the baselines are
available at https://project.inria.fr/stressid/.

Submission Number: 733