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Estimation of wakefulness in video-based lectures based on multimodal data fusion.

, , , , and . UbiComp/ISWC Adjunct, page 50-53. ACM, (2020)

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Detecting Drowsy Learners at the Wheel of e-Learning Platforms with Multimodal Learning Analytics, , , , , , and . IEEE Access, (2021)Concentration Estimation in E-Learning Based on Learner's Facial Reaction to Teacher's Action., and . IUI Companion, page 103-104. ACM, (2020)Uncertainty Prediction for Facial Action Units Recognition under Degraded Conditions., , , , , and . ICMLA, page 431-436. IEEE, (2022)Engagement estimation based on synchrony of head movements: application to actual e-learning scenarios., , and . IUI Companion, page 25-26. ACM, (2019)MIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Facial Expression Recognition., , , and . WACV, page 6538-6548. IEEE, (2024)Estimation of wakefulness in video-based lectures based on multimodal data fusion., , , , and . UbiComp/ISWC Adjunct, page 50-53. ACM, (2020)Detecting Learner Drowsiness Based on Facial Expressions and Head Movements in Online Courses., , , , , , , and . IUI Companion, page 124-125. ACM, (2020)Mental fatigue estimation based on luminance changes in facial images., , and . SII, page 526-531. IEEE, (2016)Facial Action Unit Detection Based on Teacher-Student Learning Framework for Partially Occluded Facial Images., and . FG, page 1-5. IEEE, (2021)Mental fatigue estimation based on facial expressions during speech., , and . SII, page 223-228. IEEE, (2015)