Measurement of Mental Workload Using Heart Rate Variability and Electrodermal Activity
Mental workload reflects the cognitive demands placed on individuals when performing tasks, particularly under pressure. As the esports industry continues to expand rapidly, understanding and managing the mental workload of esports athletes is crucial due to its impact on performance. While self-rep...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10813170/ |
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| author | Aisy Al Fawwaz Osmalina Nur Rahma Khusnul Ain Sayyidul Istighfar Ittaqillah Rifai Chai |
| author_facet | Aisy Al Fawwaz Osmalina Nur Rahma Khusnul Ain Sayyidul Istighfar Ittaqillah Rifai Chai |
| author_sort | Aisy Al Fawwaz |
| collection | DOAJ |
| description | Mental workload reflects the cognitive demands placed on individuals when performing tasks, particularly under pressure. As the esports industry continues to expand rapidly, understanding and managing the mental workload of esports athletes is crucial due to its impact on performance. While self-reported measures offer insights into players’ subjective experiences, this study integrates these reports with objective physiological data—heart rate variability (HRV) and electrodermal activity (EDA)—to comprehensively assess mental workload. Data from 96 participants over 21 competitive matches were analyzed, revealing significant autonomic responses linked to cognitive demands. Key predictors of workload, such as Tonic Peak Count and Phasic Peak Count, were identified. Using machine learning models like support vector machine (SVM), workload classification achieved an accuracy of 81.97% and an AUC of 0.8824. These results underscore the value of combining physiological and subjective metrics to enable real-time monitoring and provide actionable insights for enhancing performance and well-being in high-pressure esports settings. |
| format | Article |
| id | doaj-art-48775f410e5549f584c995d9b75cfe34 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-48775f410e5549f584c995d9b75cfe342025-01-01T00:01:31ZengIEEEIEEE Access2169-35362024-01-011219758919760110.1109/ACCESS.2024.352164910813170Measurement of Mental Workload Using Heart Rate Variability and Electrodermal ActivityAisy Al Fawwaz0https://orcid.org/0009-0005-6134-8726Osmalina Nur Rahma1https://orcid.org/0000-0003-2712-6191Khusnul Ain2Sayyidul Istighfar Ittaqillah3Rifai Chai4https://orcid.org/0000-0002-1922-7024Biomedical Engineering, Faculty of Science and Technology, Universitas Airlangga, Surabaya, IndonesiaBiomedical Engineering, Faculty of Science and Technology, Universitas Airlangga, Surabaya, IndonesiaBiomedical Engineering, Faculty of Science and Technology, Universitas Airlangga, Surabaya, IndonesiaBiomedical Engineering, Faculty of Science and Technology, Universitas Airlangga, Surabaya, IndonesiaDepartment of Engineering Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC, AustraliaMental workload reflects the cognitive demands placed on individuals when performing tasks, particularly under pressure. As the esports industry continues to expand rapidly, understanding and managing the mental workload of esports athletes is crucial due to its impact on performance. While self-reported measures offer insights into players’ subjective experiences, this study integrates these reports with objective physiological data—heart rate variability (HRV) and electrodermal activity (EDA)—to comprehensively assess mental workload. Data from 96 participants over 21 competitive matches were analyzed, revealing significant autonomic responses linked to cognitive demands. Key predictors of workload, such as Tonic Peak Count and Phasic Peak Count, were identified. Using machine learning models like support vector machine (SVM), workload classification achieved an accuracy of 81.97% and an AUC of 0.8824. These results underscore the value of combining physiological and subjective metrics to enable real-time monitoring and provide actionable insights for enhancing performance and well-being in high-pressure esports settings.https://ieeexplore.ieee.org/document/10813170/Mental workloadesports performanceheart rate variabilityelectrodermal activitymachine learning classification |
| spellingShingle | Aisy Al Fawwaz Osmalina Nur Rahma Khusnul Ain Sayyidul Istighfar Ittaqillah Rifai Chai Measurement of Mental Workload Using Heart Rate Variability and Electrodermal Activity IEEE Access Mental workload esports performance heart rate variability electrodermal activity machine learning classification |
| title | Measurement of Mental Workload Using Heart Rate Variability and Electrodermal Activity |
| title_full | Measurement of Mental Workload Using Heart Rate Variability and Electrodermal Activity |
| title_fullStr | Measurement of Mental Workload Using Heart Rate Variability and Electrodermal Activity |
| title_full_unstemmed | Measurement of Mental Workload Using Heart Rate Variability and Electrodermal Activity |
| title_short | Measurement of Mental Workload Using Heart Rate Variability and Electrodermal Activity |
| title_sort | measurement of mental workload using heart rate variability and electrodermal activity |
| topic | Mental workload esports performance heart rate variability electrodermal activity machine learning classification |
| url | https://ieeexplore.ieee.org/document/10813170/ |
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