Multimodal Vigilance Estimation Using Deep Learning.
IEEE Trans Cybern
; 52(5): 3097-3110, 2022 May.
Article
en En
| MEDLINE
| ID: mdl-33027022
The phenomenon of increasing accidents caused by reduced vigilance does exist. In the future, the high accuracy of vigilance estimation will play a significant role in public transportation safety. We propose a multimodal regression network that consists of multichannel deep autoencoders with subnetwork neurons (MCDAE sn ). After we define two thresholds of "0.35" and "0.70" from the percentage of eye closure, the output values are in the continuous range of 0-0.35, 0.36-0.70, and 0.71-1 representing the awake state, the tired state, and the drowsy state, respectively. To verify the efficiency of our strategy, we first applied the proposed approach to a single modality. Then, for the multimodality, since the complementary information between forehead electrooculography and electroencephalography features, we found the performance of the proposed approach using features fusion significantly improved, demonstrating the effectiveness and efficiency of our method.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Vigilia
/
Aprendizaje Profundo
Idioma:
En
Revista:
IEEE Trans Cybern
Año:
2022
Tipo del documento:
Article
Pais de publicación:
Estados Unidos