A framework to estimate cognitive load using physiological data
| Autor: | Ingo Keller, Muneeb Imtiaz Ahmad, David A. Robb, Katrin Solveig Lohan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 05 social sciences Human error Robotics Management Science and Operations Research Machine learning computer.software_genre Automation 050105 experimental psychology Computer Science Applications Random forest Naive Bayes classifier User experience design Hardware and Architecture 0501 psychology and cognitive sciences Artificial intelligence business Set (psychology) computer 050107 human factors Cognitive load |
| Popis: | Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load. |
| Databáze: | OpenAIRE |
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