Online ECG-based Features for Cognitive Load Assessment
| Autor: | Matthew D. Wilson, P. C. Caroline Chanel, Sébastien Scannella |
|---|---|
| Přispěvatelé: | Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Biotechnologies Machine learning computer.software_genre Standard deviation 03 medical and health sciences 0302 clinical medicine 0501 psychology and cognitive sciences 050102 behavioral science & comparative psychology Online algorithm business.industry 05 social sciences Neurosciences Workload Linear discriminant analysis Classification Support vector machine Statistical classification Online cognitive load assessment Feature (computer vision) Human-machine interaction ECG features Task analysis Artificial intelligence business computer 030217 neurology & neurosurgery Cognitive load |
| Zdroj: | SMC |
| Popis: | This study was concerned with the development and testing of online cognitive-load monitoring methods by means of a working-memory experiment using electrocardiogram (ECG) analyses for future applications in mixed-initiative human-machine interaction (HMI). To this end, we first identified potentially reliable cognitive-workload-related cardiac metrics and algorithms for online processing. We then compared our online results to those conventionally obtained with state-of-the-art offline methods. Finally, we evaluated the possibility of classifying low versus high working-memory load using different classification algorithms. Our results show that both offline and online methods reliably estimate the workload associated with a multi-level working-memory task at the group level, whether it is with the heart rhythm or the heart rate variation (standard deviation of the RR interval). Moreover, we found significant working-memory load classification accuracy using both two-dimensional linear discriminant analyses (LDA) or a support vector machine (SVM). We hence argue that our online algorithm is reliable enough to provide online electrocardiographic metrics as a tool for real-life workload evaluation and can be a valuable feature for mixed-initiative systems. |
| Databáze: | OpenAIRE |
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