Neuroergonomic Assessment of Wheelchair Control Using Mobile fNIRS

Shawn Joshi, Roxana Ramirez Herrera, Daniella Nicole Springett, Benjamin David Weedon, Dafne Zuleima Morgado Ramirez, Catherine Holloway, Helen Dawes, Hasan Ayaz

Research output: Contribution to journalArticlepeer-review

7 Citations (SciVal)
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For over two centuries, the wheelchair has been one of the most common assistive devices for individuals with locomotor impairments without many modifications. Wheelchair control is a complex motor task that increases both the physical and cognitive workload. New wheelchair interfaces, including Power Assisted devices, can further augment users by reducing the required physical effort, however little is known on the mental effort implications. In this study, we adopted a neuroergonomic approach utilizing mobile and wireless functional near infrared spectroscopy (fNIRS) based brain monitoring of physically active participants. 48 volunteers (30 novice and 18 experienced) self-propelled on a wheelchair with and without a PowerAssist interface in both simple and complex realistic environments. Results indicated that as expected, the complex more difficult environment led to lower task performance complemented by higher prefrontal cortex activity compared to the simple environment. The use of the PowerAssist feature had significantly lower brain activation compared to traditional manual control only for novices. Expertise led to a lower brain activation pattern within the middle frontal gyrus, complemented by performance metrics that involve lower cognitive workload. Results here confirm the potential of the Neuroergonomic approach and that direct neural activity measures can complement and enhance task performance metrics. We conclude that the cognitive workload benefits of PowerAssist are more directed to new users and difficult settings. The approach demonstrated here can be utilized in future studies to enable greater personalization and understanding of mobility interfaces within real-world dynamic environments.

Original languageEnglish
Article number9086088
Pages (from-to)1488-1496
Number of pages9
JournalIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Issue number6
Publication statusPublished - 4 May 2020


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