FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3145728983> ?p ?o ?g. }

• W3145728983 endingPage "2381" @default.
• W3145728983 startingPage "2381" @default.
• W3145728983 abstract "Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s)." @default.
• W3145728983 created "2021-04-13" @default.
• W3145728983 creator A5003464443 @default.
• W3145728983 creator A5007533948 @default.
• W3145728983 creator A5019869840 @default.
• W3145728983 date "2021-03-30" @default.
• W3145728983 modified "2023-10-15" @default.
• W3145728983 title "Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals" @default.
• W3145728983 cites W1964029730 @default.
• W3145728983 cites W1972304037 @default.
• W3145728983 cites W1975183513 @default.
• W3145728983 cites W2000503364 @default.
• W3145728983 cites W2040488623 @default.
• W3145728983 cites W2067689261 @default.
• W3145728983 cites W2079155753 @default.
• W3145728983 cites W2081681829 @default.
• W3145728983 cites W2117866167 @default.
• W3145728983 cites W2171801645 @default.
• W3145728983 cites W2291344655 @default.
• W3145728983 cites W2462602202 @default.
• W3145728983 cites W2522580204 @default.
• W3145728983 cites W2550780661 @default.
• W3145728983 cites W2559747093 @default.
• W3145728983 cites W2593216954 @default.
• W3145728983 cites W2773478070 @default.
• W3145728983 cites W2785419426 @default.
• W3145728983 cites W2891394872 @default.
• W3145728983 cites W2899202652 @default.
• W3145728983 cites W2900636181 @default.
• W3145728983 cites W2937247086 @default.
• W3145728983 cites W2953913529 @default.
• W3145728983 cites W2954948187 @default.
• W3145728983 cites W2957344258 @default.
• W3145728983 cites W2973178301 @default.
• W3145728983 cites W2974802536 @default.
• W3145728983 cites W2984913259 @default.
• W3145728983 cites W2996665814 @default.
• W3145728983 cites W3003491317 @default.
• W3145728983 cites W3005796278 @default.
• W3145728983 cites W3007732545 @default.
• W3145728983 cites W3009849862 @default.
• W3145728983 cites W3010734943 @default.
• W3145728983 cites W3016677239 @default.
• W3145728983 cites W3017334854 @default.
• W3145728983 cites W3090179467 @default.
• W3145728983 cites W3118831461 @default.
• W3145728983 doi "https://doi.org/10.3390/s21072381" @default.
• W3145728983 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8038071" @default.
• W3145728983 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33808147" @default.
• W3145728983 hasPublicationYear "2021" @default.
• W3145728983 type Work @default.
• W3145728983 sameAs 3145728983 @default.
• W3145728983 citedByCount "10" @default.
• W3145728983 countsByYear W31457289832021 @default.
• W3145728983 countsByYear W31457289832022 @default.
• W3145728983 countsByYear W31457289832023 @default.
• W3145728983 crossrefType "journal-article" @default.
• W3145728983 hasAuthorship W3145728983A5003464443 @default.
• W3145728983 hasAuthorship W3145728983A5007533948 @default.
• W3145728983 hasAuthorship W3145728983A5019869840 @default.
• W3145728983 hasBestOaLocation W31457289831 @default.
• W3145728983 hasConcept C121332964 @default.
• W3145728983 hasConcept C12267149 @default.
• W3145728983 hasConcept C138885662 @default.
• W3145728983 hasConcept C153180895 @default.
• W3145728983 hasConcept C154945302 @default.
• W3145728983 hasConcept C158622935 @default.
• W3145728983 hasConcept C17744445 @default.
• W3145728983 hasConcept C199360897 @default.
• W3145728983 hasConcept C199539241 @default.
• W3145728983 hasConcept C21036866 @default.
• W3145728983 hasConcept C2776359362 @default.
• W3145728983 hasConcept C2779843651 @default.
• W3145728983 hasConcept C28490314 @default.
• W3145728983 hasConcept C41008148 @default.
• W3145728983 hasConcept C41895202 @default.
• W3145728983 hasConcept C61797465 @default.
• W3145728983 hasConcept C62520636 @default.
• W3145728983 hasConcept C81363708 @default.
• W3145728983 hasConcept C94625758 @default.
• W3145728983 hasConceptScore W3145728983C121332964 @default.
• W3145728983 hasConceptScore W3145728983C12267149 @default.
• W3145728983 hasConceptScore W3145728983C138885662 @default.
• W3145728983 hasConceptScore W3145728983C153180895 @default.
• W3145728983 hasConceptScore W3145728983C154945302 @default.
• W3145728983 hasConceptScore W3145728983C158622935 @default.
• W3145728983 hasConceptScore W3145728983C17744445 @default.
• W3145728983 hasConceptScore W3145728983C199360897 @default.
• W3145728983 hasConceptScore W3145728983C199539241 @default.
• W3145728983 hasConceptScore W3145728983C21036866 @default.
• W3145728983 hasConceptScore W3145728983C2776359362 @default.
• W3145728983 hasConceptScore W3145728983C2779843651 @default.
• W3145728983 hasConceptScore W3145728983C28490314 @default.
• W3145728983 hasConceptScore W3145728983C41008148 @default.
• W3145728983 hasConceptScore W3145728983C41895202 @default.
• W3145728983 hasConceptScore W3145728983C61797465 @default.
• W3145728983 hasConceptScore W3145728983C62520636 @default.
• W3145728983 hasConceptScore W3145728983C81363708 @default.

• next