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arXiv · 2304.12470

Vision-based Estimation of Fatigue and Engagement in Cognitive Training Sessions

Yanchen Wang·Adam Turnbull·Yunlong Xu·Kathi Heffner·Feng Vankee Lin·Ehsan Adeli
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Abstract

Computerized cognitive training (CCT) is a scalable, well-tolerated intervention that has promise for slowing cognitive decline. Outcomes from CCT are limited by a lack of effective engagement, which is decreased by factors such as mental fatigue, particularly in older adults at risk for dementia. There is a need for scalable, automated measures that can monitor mental fatigue during CCT. Here, we develop and validate a novel Recurrent Video Transformer (RVT) method for monitoring real-time mental fatigue in older adults with mild cognitive impairment from video-recorded facial gestures during CCT. The RVT model achieved the highest balanced accuracy(78%) and precision (0.82) compared to the prior state-of-the-art models for binary and multi-class classification of mental fatigue and was additionally validated via significant association (p=0.023) with CCT reaction time. By leveraging dynamic temporal information, the RVT model demonstrates the potential to accurately measure real-time mental fatigue, laying the foundation for future personalized CCT that increase effective engagement.

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Yanchen Wang, Adam Turnbull, Yunlong Xu, Kathi Heffner, Feng Vankee Lin, Ehsan Adeli. 2023-04-24. Vision-based Estimation of Fatigue and Engagement in Cognitive Training Sessions. https://arxiv.org/abs/2304.12470

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