Vision-based Estimation of Fatigue and Engagement in Cognitive Training Sessions
This addresses the need for scalable, automated fatigue monitoring to improve engagement in cognitive training for older adults at risk for dementia, representing a domain-specific incremental advance.
The paper tackled the problem of monitoring mental fatigue during computerized cognitive training (CCT) in older adults with mild cognitive impairment, developing a Recurrent Video Transformer (RVT) method that achieved 78% balanced accuracy and 0.82 precision for fatigue classification, validated via association with reaction time.
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.