Martin Gjoreski (Author), Tine Kolenik (Author), Timotej Knez (Author), Mitja Luštrek (Author), Matjaž Gams (Author), Hristijan Gjoreski (Author), Veljko Pejović (Author)

Abstract

This study introduces two datasets for multimodal research on cognitive load inference and personality traits. Different to other datasets in Affective Computing, which disregard participants’ personality traits or focus only on emotions, stress, or cognitive load from one specific task, the participants in our experiments performed seven different tasks in total. In the first dataset, 23 participants played a varying difficulty (easy, medium, and hard) game on a smartphone. In the second dataset, 23 participants performed six psychological tasks on a PC, again with varying difficulty. In both experiments, the participants filled personality trait questionnaires and marked their perceived cognitive load using NASA-TLX after each task. Additionally, the participants’ physiological response was recorded using a wrist device measuring heart rate, beat-to-beat intervals, galvanic skin response, skin temperature, and three-axis acceleration. The datasets allow multimodal study of physiological responses of individuals in relation to their personality and cognitive load. Various analyses of relationships between personality traits, subjective cognitive load (i.e., NASA-TLX), and objective cognitive load (i.e., task difficulty) are presented. Additionally, baseline machine learning models for recognizing task difficulty are presented, including a multitask learning (MTL) neural network that outperforms single-task neural network by simultaneously learning from the two datasets. The datasets are publicly available to advance the field of cognitive load inference using commercially available devices.

Keywords

cognitive load;dataset;Affective Computing;machine learning;physiology;personality traits;sensor data;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FRI - Faculty of Computer and Information Science
UDC: 004.8
COBISS: 17709571 Link will open in a new window
ISSN: 2076-3417
Views: 182
Downloads: 66
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Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Type (COBISS): Article
Pages: str. 3843-1-3843-21
Volume: ǂVol. ǂ10
Issue: ǂno. ǂ11
Chronology: 2020
DOI: 10.3390/app10113843
ID: 14083835