Assessment of Mental Workload: a Comparison of Machine Learning Methods and Subjective Assessment Techniques
| العنوان: | Assessment of Mental Workload: a Comparison of Machine Learning Methods and Subjective Assessment Techniques |
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| المؤلفون: | Saturnino Luz, Karim Moustafa, Luca Longo |
| المصدر: | Articles Communications in Computer and Information Science ISBN: 9783319610603 H-WORKLOAD |
| بيانات النشر: | Technological University Dublin, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | Computational model, assessments, human performance, Computer science, business.industry, Concurrent validity, 020207 software engineering, Workload, 02 engineering and technology, computer.software_genre, Machine learning, Field (computer science), Task (project management), Support vector machine, mental workloads, Convergent validity, Multidisciplinary approach, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Data mining, Artificial intelligence, computer interfaces, Computer Engineering, business, computer |
| الوصف: | Mental workload (MWL) measurement is a complex multidisciplinary research field. In the last 50 years of research endeavour, MWL measurement has mainly produced theory-driven models. Some of the reasons for justifying this trend includes the omnipresent uncertainty about how to define the construct of MWL and the limited use of data-driven research methodologies. This work presents novel research focused on the investigation of the capability of a selection of supervised Machine Learning (ML) classification techniques to produce data-driven computational models of MWL for the prediction of objective performance. These are then compared to two state-of-the-art subjective techniques for the assessment of MWL, namely the NASA Task Load Index and the Workload Profile, through an analysis of their concurrent and convergent validity. Findings show that the data-driven models generally tend to outperform the two baseline selected techniques. |
| وصف الملف: | application/pdf |
| ردمك: | 978-3-319-61060-3 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ada0fb436a116d8ae446617acff73144Test https://arrow.tudublin.ie/context/scschcomart/article/1066/viewcontent/Assessment_of_Mental_Workload___a_Comparison_of_Machine_Learning_Methods_and_Subjective_Assessment_Techniques.pdfTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....ada0fb436a116d8ae446617acff73144 |
| قاعدة البيانات: | OpenAIRE |
| ردمك: | 9783319610603 |
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