Datasets for cognitive load inference using wearable sensors and psychological traits

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

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.

• Language: English
• Keywords: cognitive load, dataset, Affective Computing, machine learning, physiology, personality traits, sensor data
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2020
• Number of pages: 21 str.
• Numbering: Vol. 10, iss. 11, art. 3843
• PID: 20.500.12556/RUL-133713
• UDC: 004.8
• ISSN on article: 2076-3417
• DOI: 10.3390/app10113843
• COBISS.SI-ID: 17709571
• Publication date in RUL: 10.12.2021

Record is a part of a journal

• Title: Applied sciences
• Shortened title: Appl. sci.
• Publisher: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 01.06.2020

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: U2-AG-16/0672, 0287

• Funder: ARRS - Slovenian Research Agency
• Project number: N2-0136
• Name: Povečanje učinkovitosti uporabe virov na pametnih telefonih s pomočjo približnega računanja