Next Article in Journal
True Equalization of Polarization-Dependent Loss in Presence of Fast Rotation of SOP
Next Article in Special Issue
Service-Aware Interactive Presentation of Items for Decision-Making
Previous Article in Journal
Automated Detection and Segmentation of Early Gastric Cancer from Endoscopic Images Using Mask R-CNN
Article

Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits

1
Jožef Stefan Institute, 1000 Ljubljana, Slovenia
2
Jožef Stefan Postgraduate School, 1000 Ljubljana, Slovenia
3
Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
4
Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2020, 10(11), 3843; https://doi.org/10.3390/app10113843
Received: 10 April 2020 / Revised: 14 May 2020 / Accepted: 28 May 2020 / Published: 31 May 2020
(This article belongs to the Special Issue Implicit and Explicit Human-Computer Interaction)
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. View Full-Text
Keywords: cognitive load; dataset; Affective Computing; machine learning; physiology; personality traits; sensor data cognitive load; dataset; Affective Computing; machine learning; physiology; personality traits; sensor data
Show Figures

Figure 1

MDPI and ACS Style

Gjoreski, M.; Kolenik, T.; Knez, T.; Luštrek, M.; Gams, M.; Gjoreski, H.; Pejović, V. Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits. Appl. Sci. 2020, 10, 3843. https://doi.org/10.3390/app10113843

AMA Style

Gjoreski M, Kolenik T, Knez T, Luštrek M, Gams M, Gjoreski H, Pejović V. Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits. Applied Sciences. 2020; 10(11):3843. https://doi.org/10.3390/app10113843

Chicago/Turabian Style

Gjoreski, Martin, Tine Kolenik, Timotej Knez, Mitja Luštrek, Matjaž Gams, Hristijan Gjoreski, and Veljko Pejović. 2020. "Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits" Applied Sciences 10, no. 11: 3843. https://doi.org/10.3390/app10113843

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop