Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
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 partici...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/11/3843 |
| _version_ | 1797566487392681984 |
|---|---|
| author | Martin Gjoreski Tine Kolenik Timotej Knez Mitja Luštrek Matjaž Gams Hristijan Gjoreski Veljko Pejović |
| author_facet | Martin Gjoreski Tine Kolenik Timotej Knez Mitja Luštrek Matjaž Gams Hristijan Gjoreski Veljko Pejović |
| author_sort | Martin Gjoreski |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:27:32Z |
| format | Article |
| id | doaj.art-3b51c004d94c43559fb3cf89c236cb5d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:27:32Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3b51c004d94c43559fb3cf89c236cb5d2023-11-20T02:24:07ZengMDPI AGApplied Sciences2076-34172020-05-011011384310.3390/app10113843Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological TraitsMartin Gjoreski0Tine Kolenik1Timotej Knez2Mitja Luštrek3Matjaž Gams4Hristijan Gjoreski5Veljko Pejović6Jožef Stefan Institute, 1000 Ljubljana, SloveniaJožef Stefan Institute, 1000 Ljubljana, SloveniaFaculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, SloveniaJožef Stefan Institute, 1000 Ljubljana, SloveniaJožef Stefan Institute, 1000 Ljubljana, SloveniaFaculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, 1000 Skopje, North MacedoniaFaculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, SloveniaThis 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.https://www.mdpi.com/2076-3417/10/11/3843cognitive loaddatasetAffective Computingmachine learningphysiologypersonality traits |
| spellingShingle | Martin Gjoreski Tine Kolenik Timotej Knez Mitja Luštrek Matjaž Gams Hristijan Gjoreski Veljko Pejović Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits Applied Sciences cognitive load dataset Affective Computing machine learning physiology personality traits |
| title | Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits |
| title_full | Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits |
| title_fullStr | Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits |
| title_full_unstemmed | Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits |
| title_short | Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits |
| title_sort | datasets for cognitive load inference using wearable sensors and psychological traits |
| topic | cognitive load dataset Affective Computing machine learning physiology personality traits |
| url | https://www.mdpi.com/2076-3417/10/11/3843 |
| work_keys_str_mv | AT martingjoreski datasetsforcognitiveloadinferenceusingwearablesensorsandpsychologicaltraits AT tinekolenik datasetsforcognitiveloadinferenceusingwearablesensorsandpsychologicaltraits AT timotejknez datasetsforcognitiveloadinferenceusingwearablesensorsandpsychologicaltraits AT mitjalustrek datasetsforcognitiveloadinferenceusingwearablesensorsandpsychologicaltraits AT matjazgams datasetsforcognitiveloadinferenceusingwearablesensorsandpsychologicaltraits AT hristijangjoreski datasetsforcognitiveloadinferenceusingwearablesensorsandpsychologicaltraits AT veljkopejovic datasetsforcognitiveloadinferenceusingwearablesensorsandpsychologicaltraits |