Matches in SemOpenAlex for { <https://semopenalex.org/work/W2897034612> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W2897034612 abstract "Driving is an activity that can induce significant levels of negative emotion, such as stress and anger. These negative emotions occur naturally in everyday life, but frequent episodes can be detrimental to cardiovascular health in the long term. The development of monitoring systems to detect negative emotions often rely on labels derived from subjective self-report. However, this approach is burdensome, intrusive, low fidelity (i.e. scales are administered infrequently) and places huge reliance on the veracity of subjective self-report. This paper explores an alternative approach that provides greater fidelity by using psychophysiological data (e.g. heart rate) to dynamically label data derived from the driving task (e.g. speed, road type). A number of different techniques for generating labels for machine learning were compared: 1) deriving labels from subjective self-report and 2) labelling data via psychophysiological activity (e.g. heart rate (HR), pulse transit time (PTT), etc.) to create dynamic labels of high vs. low anxiety for each participant. The classification accuracy associated with both labelling techniques was evaluated using Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Results indicated that classification of driving data using subjective labelled data (1) achieved a maximum AUC of 73%, whilst the labels derived from psychophysiological data (2) achieved equivalent performance of 74%. Whilst classification performance was similar, labelling driving data via psychophysiology offers a number of advantages over self-reports, e.g. implicit, dynamic, objective, high fidelity." @default.
- W2897034612 created "2018-10-26" @default.
- W2897034612 creator A5015255525 @default.
- W2897034612 creator A5059828688 @default.
- W2897034612 date "2018-03-01" @default.
- W2897034612 modified "2023-09-23" @default.
- W2897034612 title "Detecting Negative Emotions During Real-Life Driving via Dynamically Labelled Physiological Data" @default.
- W2897034612 cites W1570713908 @default.
- W2897034612 cites W1790787857 @default.
- W2897034612 cites W1958676502 @default.
- W2897034612 cites W1968829280 @default.
- W2897034612 cites W1972500128 @default.
- W2897034612 cites W1993026314 @default.
- W2897034612 cites W1995884363 @default.
- W2897034612 cites W2005511405 @default.
- W2897034612 cites W2014081548 @default.
- W2897034612 cites W2090939247 @default.
- W2897034612 cites W2108231746 @default.
- W2897034612 cites W2116488983 @default.
- W2897034612 cites W2156395333 @default.
- W2897034612 cites W2158526972 @default.
- W2897034612 cites W2339017103 @default.
- W2897034612 cites W2520289546 @default.
- W2897034612 cites W2522580204 @default.
- W2897034612 cites W2559137209 @default.
- W2897034612 cites W3098017922 @default.
- W2897034612 cites W4302202620 @default.
- W2897034612 doi "https://doi.org/10.1109/percomw.2018.8480369" @default.
- W2897034612 hasPublicationYear "2018" @default.
- W2897034612 type Work @default.
- W2897034612 sameAs 2897034612 @default.
- W2897034612 citedByCount "4" @default.
- W2897034612 countsByYear W28970346122019 @default.
- W2897034612 countsByYear W28970346122020 @default.
- W2897034612 countsByYear W28970346122022 @default.
- W2897034612 crossrefType "proceedings-article" @default.
- W2897034612 hasAuthorship W2897034612A5015255525 @default.
- W2897034612 hasAuthorship W2897034612A5059828688 @default.
- W2897034612 hasBestOaLocation W28970346122 @default.
- W2897034612 hasConcept C107457646 @default.
- W2897034612 hasConcept C138496976 @default.
- W2897034612 hasConcept C15744967 @default.
- W2897034612 hasConcept C3020014160 @default.
- W2897034612 hasConcept C41008148 @default.
- W2897034612 hasConceptScore W2897034612C107457646 @default.
- W2897034612 hasConceptScore W2897034612C138496976 @default.
- W2897034612 hasConceptScore W2897034612C15744967 @default.
- W2897034612 hasConceptScore W2897034612C3020014160 @default.
- W2897034612 hasConceptScore W2897034612C41008148 @default.
- W2897034612 hasLocation W28970346121 @default.
- W2897034612 hasLocation W28970346122 @default.
- W2897034612 hasOpenAccess W2897034612 @default.
- W2897034612 hasPrimaryLocation W28970346121 @default.
- W2897034612 hasRelatedWork W1536570095 @default.
- W2897034612 hasRelatedWork W2045456578 @default.
- W2897034612 hasRelatedWork W2076610045 @default.
- W2897034612 hasRelatedWork W2278205256 @default.
- W2897034612 hasRelatedWork W2883555950 @default.
- W2897034612 hasRelatedWork W2918883224 @default.
- W2897034612 hasRelatedWork W3204394973 @default.
- W2897034612 hasRelatedWork W4246426965 @default.
- W2897034612 hasRelatedWork W68053931 @default.
- W2897034612 hasRelatedWork W3106945349 @default.
- W2897034612 isParatext "false" @default.
- W2897034612 isRetracted "false" @default.
- W2897034612 magId "2897034612" @default.
- W2897034612 workType "article" @default.