4.7 Article

Data-driven learning fatigue detection system: A multimodal fusion approach of ECG (electrocardiogram) and video signals

期刊

MEASUREMENT
卷 201, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2022.111648

关键词

Deep learning (DL); Fatigue; Feature fusion; Learning analytics; Physiological signal; Video

资金

  1. National Key R & D Program of China [2020AAA0108804]
  2. National Natural Science Foundation of China [61937001]
  3. National Natural Science Foundation of Hubei Province [2021CFB157]

向作者/读者索取更多资源

Fatigue can lead to low efficiency and accidents. Detecting fatigue in the field of education can improve learning efficiency. This study develops a multimodal learning fatigue detection system using ECG and video signals to classify a learner's state into alert, normal, and fatigued. Experimental results show that the system outperforms other methods, achieving detection accuracies of 99.6% and 91.8% on two datasets.
Fatigue could lead to low efficiency and even serious disaster. In the educational field, detecting fatigue could help adjust teaching strategies accordingly when a student is inactive, which can potentially improve learning efficiency. Despite numerous studies in fatigue detection, there is still a lack of multiple classifier systems capable of detecting fatigue in daily life (without specific stimulations). To initially alleviate this problem, this study develops a learning fatigue detection system using a multimodal approach with ECG and video signals, classi-fying a learner's state into three categories: alert, normal, and fatigued. To validate performance, the proposed system is tested on (i) an open-source dataset DROZY (n = 35) and (ii) a self-collected dataset captured in a learning environment (n = 92). The experimental results based on 10-fold cross-validation demonstrate that the system outperforms the state-of-the-art approaches, achieving a detection accuracy of 99.6% and 91.8% on the two datasets, respectively.

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