Vision-based estimation of fatigue and engagement in cognitive training sessions.
Artif Intell Med
; 154: 102923, 2024 08.
Article
em En
| MEDLINE
| ID: mdl-38970987
ABSTRACT
Computerized cognitive training (CCT) is a scalable, well-tolerated intervention that has promise for slowing cognitive decline. The effectiveness of CCT is often affected by a lack of effective engagement. Mental fatigue is a the primary factor for compromising effective engagement in CCT, particularly in older adults at risk for dementia. There is a need for scalable, automated measures that can constantly monitor and reliably detect 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 using their video-recorded facial gestures during CCT. The RVT model achieved the highest balanced accuracy (79.58%) and precision (0.82) compared to the prior models for binary and multi-class classification of mental fatigue. We also validated our model by significantly relating to reaction time across CCT tasks (Waldχ2=5.16,p=0.023). By leveraging dynamic temporal information, the RVT model demonstrates the potential to accurately measure real-time mental fatigue, laying the foundation for future CCT research aiming to enhance effective engagement by timely prevention of mental fatigue.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fadiga Mental
/
Disfunção Cognitiva
/
Treino Cognitivo
Limite:
Aged
/
Aged80
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
Artif Intell Med
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos
País de publicação:
Holanda