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Cognitive Workload Assessment During Complex Coordinated Motor Tasks in Real-world Environments

Thursday, April 1, 2021

10:00 AM-12:00 PM

BIOMED PhD Thesis Defense
Cognitive Workload Assessment During Complex Coordinated Motor Tasks in Real-world Environments with Both Healthy and Clinical Populations

Shawn Joshi, MD/PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Hasan Ayaz, PhD
Associate Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Neuroergonomic assessments of people with disabilities within real-world settings has been challenging due to environmental and practical limitations. As many of the interventions for people with disabilities involve physical activity or complex assistive devices, simultaneous measurement of the brain during unconstrained action has been typically unattainable.

Wearable and mobile neuroimaging for localized brain activity monitoring using Functional Near Infrared Spectroscopy (fNIRS) offers a unique opportunity in understanding natural cognitive processes, and specifically, the workload of disability populations performing complex coordinated motor tasks in active and realistic settings. Cognitive workload refers to limited processing capacity of the brain required by task demands. Often when an individual’s processing capacity reaches its limit, performance breakdown and errors will occur. Among disability populations, performance errors can lead to injury and behavioral change, negatively impacting social participation. Assessing the factors of cognitive workload for people with disabilities in real-world environments will aid in developing better rehabilitative assistive technologies and trainings in the effort of improving autonomy and equality.

To achieve this, we first evaluated the impact of Developmental Coordination Disorder, a motor learning disability, using fNIRS during a novel physical task. The typical diagnosis is complex and time-consuming. However combinatory neurocognitive and behavioral measures reveal distinct, disorder-dependent cognitive workload implications, which can be used to accelerate the identification of those who need the most intervention.

Secondly, we evaluated the impact of physical therapy as an intervention to this clinical cohort, and the effect of its withdrawal to inform clinical procedure in naïve motor task learning. Through a neuroergonomic lens, factors of motor task improvement and maintenance were evaluated.
Lastly, we assessed the immediate impact of an assistive mobility device as a disability intervention. Many individuals with disabilities may not demonstrate the same motor-learning capacity as typical individuals, and the use of an automatic assistive device eliminates the need for extensive training. We demonstrated the neuroergonomic evaluation on wheelchair control, indicating for whom and where power-assisted devices can most effectively reduce cognitive workload and improve accessibility.

By combining both brain and body measures, we were able to capture a deeper understanding of disability and the interventional impacts, along with demonstrating a key objective domain for evaluations in capacity building, and receptivity towards assistive devices. The findings of this research can be used to inform assistive technology design and rehabilitative guidance for continued improvement towards equality for people facing disabilities in unaccommodating environments.

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Natalia Broz

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