Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
| Autor: | Mitja Luštrek, Hristijan Gjoreski, Martin Gjoreski, Timotej Knez, Matjaž Gams, Veljko Pejovic, Tine Kolenik |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
media_common.quotation_subject Inference Multi-task learning lcsh:Technology 050105 experimental psychology Task (project management) lcsh:Chemistry 03 medical and health sciences 0302 clinical medicine Personality dataset 0501 psychology and cognitive sciences General Materials Science Big Five personality traits Affective computing Instrumentation lcsh:QH301-705.5 media_common Fluid Flow and Transfer Processes udc:004.8 Artificial neural network lcsh:T Process Chemistry and Technology cognitive load 05 social sciences General Engineering lcsh:QC1-999 sensor data Computer Science Applications machine learning lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Affective Computing physiology personality traits lcsh:Engineering (General). Civil engineering (General) 030217 neurology & neurosurgery Cognitive load lcsh:Physics Cognitive psychology |
| Zdroj: | Applied Sciences Volume 10 Issue 11 Applied Sciences, Vol 10, Iss 3843, p 3843 (2020) Applied sciences, vol. 10, no. 11, 3843, 2020. |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10113843 |
| Popis: | 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&rsquo 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&rsquo 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. |
| Databáze: | OpenAIRE |
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