1. Measuring Mental Workload Variations in Office Work Tasks using fNIRS
- Author
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Horia A. Maior, Sarah Sharples, Max L. Wilson, and Serena Midha
- Subjects
media_common.quotation_subject ,Human Factors and Ergonomics ,Education ,03 medical and health sciences ,0302 clinical medicine ,Neuroimaging ,Reading (process) ,Neuroergonomics ,0501 psychology and cognitive sciences ,Biological sciences ,fNIRS, Mental Workload, Office Work, Interruptions, Passive BCI, Neuroergonomics ,050107 human factors ,media_common ,05 social sciences ,Work (physics) ,General Engineering ,Cognition ,Workload ,G400 Computer Science ,Human-Computer Interaction ,Hardware and Architecture ,Functional near-infrared spectroscopy ,G440 Human-computer Interaction ,Psychology ,030217 neurology & neurosurgery ,Software ,Cognitive psychology - Abstract
The motivation behind using physiological measures to estimate cognitive activity is typically to build technology that can help people to understand themselves and their work, or indeed for systems to do so and adapt. While functional Near Infrared Spectroscopy (fNIRS) has been shown to reliably reflect manipulations of mental workload in different work tasks, we still need to establish whether fNIRS can differentiate variety within common office-like tasks in order to broaden our understanding of the factors involved in tracking them in real working conditions. 20 healthy participants (8 females, 12 males), whose work included office-like tasks, took part in a user study that investigated a) the sensitivity of fNIRS for measuring mental workload variations in representations of everyday reading and writing tasks, and b) how representations of natural interruptions are reflected in the data. Results supported fNIRS measuring PFC activation in differentiating between workload levels for reading tasks but not writing tasks in terms of increased oxygenated haemoglobin (O2Hb) and decreased deoxygenated haemoglobin (HHb), for harder conditions compared to easier conditions. There was considerable support for fNIRS in detecting changes in workload levels due to interruptions. Variations in workload levels during the interruptions could be understood in relation to spare capacity models. These findings may guide future work into sustained monitoring of cognitive activity in real-world settings.
- Published
- 2021