Authors:
Mouna Benchekroun
1
;
2
;
Dan Istrate
1
;
Vincent Zalc
1
and
Dominique Lenne
2
Affiliations:
1
Biomechanics and Bioengineering, UMR CNRS 7338, Université de Technologie de Compiègne, Compiègne, France
;
2
Heudiasyc (Heuristics and Diagnosis of Complex Systems), Université de Technologie de Compiègne, Compiègne, France
Keyword(s):
Multimodal Dataset, Emotion Recognition, Stress Detection, Physiological Data, Affective Computing.
Abstract:
Although chronic stress is proven to be very harmful to physical and mental well being, its diagnosis is punctual and nontrivial, which calls for reliable, continuous and automated stress monitoring systems that do not yet exist. Wireless biosensors offer opportunities to remotely detect and monitor mental stress levels, enabling improved diagnosis and early treatment. There are different algorithms and methods for wearable stress detection, however, only a few standard and publicly available datasets exist today. In this paper, we introduce a multi-modal high-quality stress detection dataset with details of the experimental protocol. The dataset includes physiological, behavioural and motion data from 74 subjects during a lab study. Different modalities such as electrocardiograms (ECG), photoplethysmograms (PPG), electrodermal activity (EDA), electromyograms (EMG) as well as three axis gyroscope and accelerometer data were recorded. In addition, protocol validation was achieved usin
g both subject’s self-reports and cortisol levels which is considered as gold standard for stress detection.
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