FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3018579152> ?p ?o ?g. }

• W3018579152 endingPage "2956" @default.
• W3018579152 startingPage "2956" @default.
• W3018579152 abstract "As people communicate with each other, they use gestures and facial expressions as a means to convey and understand emotional state. Non-verbal means of communication are essential to understanding, based on external clues to a person’s emotional state. Recently, active studies have been conducted on the lifecare service of analyzing users’ facial expressions. Yet, rather than a service necessary for everyday life, the service is currently provided only for health care centers or certain medical institutions. It is necessary to conduct studies to prevent accidents that suddenly occur in everyday life and to cope with emergencies. Thus, we propose facial expression analysis using line-segment feature analysis-convolutional recurrent neural network (LFA-CRNN) feature extraction for health-risk assessments of drivers. The purpose of such an analysis is to manage and monitor patients with chronic diseases who are rapidly increasing in number. To prevent automobile accidents and to respond to emergency situations due to acute diseases, we propose a service that monitors a driver’s facial expressions to assess health risks and alert the driver to risk-related matters while driving. To identify health risks, deep learning technology is used to recognize expressions of pain and to determine if a person is in pain while driving. Since the amount of input-image data is large, analyzing facial expressions accurately is difficult for a process with limited resources while providing the service on a real-time basis. Accordingly, a line-segment feature analysis algorithm is proposed to reduce the amount of data, and the LFA-CRNN model was designed for this purpose. Through this model, the severity of a driver’s pain is classified into one of nine types. The LFA-CRNN model consists of one convolution layer that is reshaped and delivered into two bidirectional gated recurrent unit layers. Finally, biometric data are classified through softmax. In addition, to evaluate the performance of LFA-CRNN, the performance was compared through the CRNN and AlexNet Models based on the University of Northern British Columbia and McMaster University (UNBC-McMaster) database." @default.
• W3018579152 created "2020-05-01" @default.
• W3018579152 creator A5016327106 @default.
• W3018579152 creator A5042241877 @default.
• W3018579152 creator A5066787824 @default.
• W3018579152 creator A5089226921 @default.
• W3018579152 date "2020-04-24" @default.
• W3018579152 modified "2023-10-14" @default.
• W3018579152 title "Driver Facial Expression Analysis Using LFA-CRNN-Based Feature Extraction for Health-Risk Decisions" @default.
• W3018579152 cites W2083310348 @default.
• W3018579152 cites W2101545465 @default.
• W3018579152 cites W2158698691 @default.
• W3018579152 cites W2161801094 @default.
• W3018579152 cites W2194187530 @default.
• W3018579152 cites W2507547138 @default.
• W3018579152 cites W2622525607 @default.
• W3018579152 cites W2735398813 @default.
• W3018579152 cites W2735984033 @default.
• W3018579152 cites W2746325398 @default.
• W3018579152 cites W2780628352 @default.
• W3018579152 cites W2789637695 @default.
• W3018579152 cites W2800170478 @default.
• W3018579152 cites W2889474744 @default.
• W3018579152 cites W2895501253 @default.
• W3018579152 cites W2901280189 @default.
• W3018579152 cites W2902298447 @default.
• W3018579152 cites W2905949437 @default.
• W3018579152 cites W2916626344 @default.
• W3018579152 cites W2939195450 @default.
• W3018579152 cites W2945495000 @default.
• W3018579152 cites W2947596481 @default.
• W3018579152 cites W2963340482 @default.
• W3018579152 cites W2963873807 @default.
• W3018579152 cites W2971147635 @default.
• W3018579152 cites W2980944777 @default.
• W3018579152 cites W2981935581 @default.
• W3018579152 cites W2987973491 @default.
• W3018579152 cites W2989897789 @default.
• W3018579152 cites W2992205873 @default.
• W3018579152 cites W2995646679 @default.
• W3018579152 cites W3002289107 @default.
• W3018579152 cites W3003436681 @default.
• W3018579152 cites W3003745987 @default.
• W3018579152 cites W2792260903 @default.
• W3018579152 cites W2907171849 @default.
• W3018579152 doi "https://doi.org/10.3390/app10082956" @default.
• W3018579152 hasPublicationYear "2020" @default.
• W3018579152 type Work @default.
• W3018579152 sameAs 3018579152 @default.
• W3018579152 citedByCount "19" @default.
• W3018579152 countsByYear W30185791522020 @default.
• W3018579152 countsByYear W30185791522021 @default.
• W3018579152 countsByYear W30185791522022 @default.
• W3018579152 countsByYear W30185791522023 @default.
• W3018579152 crossrefType "journal-article" @default.
• W3018579152 hasAuthorship W3018579152A5016327106 @default.
• W3018579152 hasAuthorship W3018579152A5042241877 @default.
• W3018579152 hasAuthorship W3018579152A5066787824 @default.
• W3018579152 hasAuthorship W3018579152A5089226921 @default.
• W3018579152 hasBestOaLocation W30185791521 @default.
• W3018579152 hasConcept C138885662 @default.
• W3018579152 hasConcept C144133560 @default.
• W3018579152 hasConcept C154945302 @default.
• W3018579152 hasConcept C162853370 @default.
• W3018579152 hasConcept C17744445 @default.
• W3018579152 hasConcept C195704467 @default.
• W3018579152 hasConcept C199360897 @default.
• W3018579152 hasConcept C199539241 @default.
• W3018579152 hasConcept C207347870 @default.
• W3018579152 hasConcept C2776401178 @default.
• W3018579152 hasConcept C2779018934 @default.
• W3018579152 hasConcept C2780378061 @default.
• W3018579152 hasConcept C41008148 @default.
• W3018579152 hasConcept C41895202 @default.
• W3018579152 hasConcept C81363708 @default.
• W3018579152 hasConcept C90559484 @default.
• W3018579152 hasConceptScore W3018579152C138885662 @default.
• W3018579152 hasConceptScore W3018579152C144133560 @default.
• W3018579152 hasConceptScore W3018579152C154945302 @default.
• W3018579152 hasConceptScore W3018579152C162853370 @default.
• W3018579152 hasConceptScore W3018579152C17744445 @default.
• W3018579152 hasConceptScore W3018579152C195704467 @default.
• W3018579152 hasConceptScore W3018579152C199360897 @default.
• W3018579152 hasConceptScore W3018579152C199539241 @default.
• W3018579152 hasConceptScore W3018579152C207347870 @default.
• W3018579152 hasConceptScore W3018579152C2776401178 @default.
• W3018579152 hasConceptScore W3018579152C2779018934 @default.
• W3018579152 hasConceptScore W3018579152C2780378061 @default.
• W3018579152 hasConceptScore W3018579152C41008148 @default.
• W3018579152 hasConceptScore W3018579152C41895202 @default.
• W3018579152 hasConceptScore W3018579152C81363708 @default.
• W3018579152 hasConceptScore W3018579152C90559484 @default.
• W3018579152 hasIssue "8" @default.
• W3018579152 hasLocation W30185791521 @default.
• W3018579152 hasLocation W30185791522 @default.
• W3018579152 hasOpenAccess W3018579152 @default.
• W3018579152 hasPrimaryLocation W30185791521 @default.
• W3018579152 hasRelatedWork W1969306251 @default.

• next