Approaches for Cognitive Load Measurement for Human-System Automation
Cognitive load experienced by humans is an important factor affecting their performance. Cognitive overload or underload may result in suboptimal human performance and may compromise safety. In driving, cognitive overload, due to various secondary tasks, such
as texting, results in distracted driving. On the other hand, cognitive underload may result in fatigue. In manufacturing, a distracted operator may be prone to muscle injuries. Similar results are likely in many other fields of human performance. Cognitive load is not directly measurable
and neither is there a unit for it. However, the change in cognitive load can be indirectly measured through various physiological, behavioral, performance based and subjective means. In this presentation, a performance metric for comparison of different metrics to determine the cognitive workload is proposed in terms of the signal to noise ratio. Using this
performance metric, several measures of cognitive load, that fall under the four broad groups of physiological, behavioral, performance based, and self-reported measures, were compared for their ability to measure changes in cognitive load. Using the proposed metrics, the cognitive load measures were compared based on data collected from 28 participants while they underwent n-back tasks. The results show that the proposed performance evaluation method can be useful to individually assess different measures of cognitive load. Finally, the role of signal processing to improve cognitive load measures is highlighted.
Date and Time
Location
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Registration
- Date: 08 Feb 2022
- Time: 11:30 AM to 12:30 PM
- All times are (GMT-05:00) Canada/Eastern
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- Ottawa, Ontario
- Canada
- Starts 30 January 2022 10:23 PM
- Ends 08 February 2022 11:30 AM
- All times are (GMT-05:00) Canada/Eastern
- No Admission Charge
Speakers
Dr. Balakumar Balasingam
Biography:
Balakumar Balasingam is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Windsor. From 2012 to 2017, he was an Assistant Research Professor in the Department of Electrical and Computer Engineering at the University of Connecticut. He received his Ph.D. in Electrical Engineering from McMaster University, Canada in 2008. After his PhD, he held a postdoctoral position at the University of Ottawa from 2008 to 2010, and then a University Postdoctoral position at the University of Connecticut from 2010 to 2012. His research interests are in signal processing, machine learning, and distributed information fusion and their applications in autonomous systems; particularly, his close interests are in battery management systems, human-machine systems and surveillance & tracking systems.