Matches in SemOpenAlex for { <https://semopenalex.org/work/W4288045348> ?p ?o ?g. }
- W4288045348 endingPage "111648" @default.
- W4288045348 startingPage "111648" @default.
- W4288045348 abstract "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, classifying 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." @default.
- W4288045348 created "2022-07-27" @default.
- W4288045348 creator A5004828218 @default.
- W4288045348 creator A5012329541 @default.
- W4288045348 creator A5015185295 @default.
- W4288045348 creator A5036846882 @default.
- W4288045348 creator A5038491612 @default.
- W4288045348 creator A5044648893 @default.
- W4288045348 creator A5061097079 @default.
- W4288045348 date "2022-09-01" @default.
- W4288045348 modified "2023-10-12" @default.
- W4288045348 title "Data-driven learning fatigue detection system: A multimodal fusion approach of ECG (electrocardiogram) and video signals" @default.
- W4288045348 cites W2029772767 @default.
- W4288045348 cites W2063923412 @default.
- W4288045348 cites W2091648408 @default.
- W4288045348 cites W2110320181 @default.
- W4288045348 cites W2124428761 @default.
- W4288045348 cites W2282606863 @default.
- W4288045348 cites W2340684451 @default.
- W4288045348 cites W2342420110 @default.
- W4288045348 cites W2486806851 @default.
- W4288045348 cites W2496052053 @default.
- W4288045348 cites W2511501509 @default.
- W4288045348 cites W2594351950 @default.
- W4288045348 cites W2727222697 @default.
- W4288045348 cites W2740051075 @default.
- W4288045348 cites W2767717765 @default.
- W4288045348 cites W2775822293 @default.
- W4288045348 cites W2778222790 @default.
- W4288045348 cites W2778978785 @default.
- W4288045348 cites W2789551001 @default.
- W4288045348 cites W2792734944 @default.
- W4288045348 cites W2794129249 @default.
- W4288045348 cites W2799906768 @default.
- W4288045348 cites W2802132828 @default.
- W4288045348 cites W2883790098 @default.
- W4288045348 cites W2890168959 @default.
- W4288045348 cites W2892075859 @default.
- W4288045348 cites W2911666939 @default.
- W4288045348 cites W2913872323 @default.
- W4288045348 cites W2942988243 @default.
- W4288045348 cites W2943512776 @default.
- W4288045348 cites W2943947171 @default.
- W4288045348 cites W2945361018 @default.
- W4288045348 cites W2947224089 @default.
- W4288045348 cites W2971555734 @default.
- W4288045348 cites W2979979528 @default.
- W4288045348 cites W2980105727 @default.
- W4288045348 cites W2980415636 @default.
- W4288045348 cites W2992422066 @default.
- W4288045348 cites W2995744782 @default.
- W4288045348 cites W2997263156 @default.
- W4288045348 cites W3002833466 @default.
- W4288045348 cites W3005537532 @default.
- W4288045348 cites W3006499155 @default.
- W4288045348 cites W3006915077 @default.
- W4288045348 cites W3008162595 @default.
- W4288045348 cites W3014095462 @default.
- W4288045348 cites W3015245549 @default.
- W4288045348 cites W3022039961 @default.
- W4288045348 cites W3032939949 @default.
- W4288045348 cites W3044317125 @default.
- W4288045348 cites W3047473789 @default.
- W4288045348 cites W3088709614 @default.
- W4288045348 cites W3093581966 @default.
- W4288045348 cites W3101616094 @default.
- W4288045348 cites W3101796369 @default.
- W4288045348 cites W3102403537 @default.
- W4288045348 cites W3103330131 @default.
- W4288045348 cites W3124354929 @default.
- W4288045348 cites W3133556082 @default.
- W4288045348 cites W3138357254 @default.
- W4288045348 cites W3159759379 @default.
- W4288045348 cites W3163337644 @default.
- W4288045348 cites W3176603448 @default.
- W4288045348 cites W3183706914 @default.
- W4288045348 cites W4200251610 @default.
- W4288045348 cites W4200588667 @default.
- W4288045348 cites W4248273320 @default.
- W4288045348 doi "https://doi.org/10.1016/j.measurement.2022.111648" @default.
- W4288045348 hasPublicationYear "2022" @default.
- W4288045348 type Work @default.
- W4288045348 citedByCount "5" @default.
- W4288045348 countsByYear W42880453482023 @default.
- W4288045348 crossrefType "journal-article" @default.
- W4288045348 hasAuthorship W4288045348A5004828218 @default.
- W4288045348 hasAuthorship W4288045348A5012329541 @default.
- W4288045348 hasAuthorship W4288045348A5015185295 @default.
- W4288045348 hasAuthorship W4288045348A5036846882 @default.
- W4288045348 hasAuthorship W4288045348A5038491612 @default.
- W4288045348 hasAuthorship W4288045348A5044648893 @default.
- W4288045348 hasAuthorship W4288045348A5061097079 @default.
- W4288045348 hasBestOaLocation W42880453481 @default.
- W4288045348 hasConcept C108583219 @default.
- W4288045348 hasConcept C119857082 @default.
- W4288045348 hasConcept C153180895 @default.
- W4288045348 hasConcept C154945302 @default.
- W4288045348 hasConcept C41008148 @default.