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A review of the use of portable technologies as observational aids in the classroom
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Joint modeling of users, questions and answers for answer selection in CQA
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Changes in science attitudes, beliefs, knowledge and physiological arousal after implementation of a multimodal, cooperative intervention in primary school science classes
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Characterising postgraduate students’ corpus query and usage patterns for disciplinary data-driven learning
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Learning factorized representations for open-set domain adaptation
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Multilevel topic dependency models for assessment design and delivery: A hypergraph based approach
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Detecting and visualizing context and stress via a fuzzy rule-based system during commuter driving
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Abstract:
Stress is a negative emotion that occurs in everyday life, such as driving. Recurrent exposure to stress can be detrimental to cardiovascular health in the long term. Nevertheless, the development of adaptive coping strategies can mitigate the influence of everyday stress on cardiovascular health. Understanding context is essential to modelling the occurrence of stress and other negative emotions during everyday life. However, driving is a highly dynamic environment, whereby the context is often described using ambiguous linguistic terms, which can be difficult to quantify. This paper proposes a Fuzzy Logic Mamdani Model to automatically estimate different categories of driving context. The system is comprised of two Membership Functions (MFs), which converts the inputs of speed and traffic density into linguistic variables. Our approach then uses these data to identify six states of driving – Idling, Journey Impedance, High Urban Workload, Low Urban Workload, High Non-Urban Workload and Low Non-Urban Workload. An interactive visualization has then been implemented that links this fuzzy logic model with psychophysiological data to identify the context of stress experienced on the road. The system has been validated using real-world data that has been collected from eight participants during their daily commuter journeys.
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Keyword:
1702 Artificial Intelligence; 1705 Computer Networks and Communications; 1706 Computer Science Applications; 1802 Information Systems and Management; Emotion Recognition; Fuzzy Logic; Mobile Health; Negative Emotion; Pervasive Computing; Physiological Computing; Stress; Wearable Computers
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URL: https://espace.library.uq.edu.au/view/UQ:f8e78be
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Discovering correlations between sparse features in distant supervision for relation extraction
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Engagement and performance in a first year natural resource science course
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Deep context of citations using machine-learning models in scholarly full-text articles
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Using an online social media space to engage parents in student learning in the early-years: enablers and impediments
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Willis, Linda-Dianne; Exley, Beryl. - : Universitat de Barcelona * Grup de Recerca Ensenyament i Aprenentatge Virtual, Observatorio de la Educacion Digital (OED), 2018
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Measuring communication difficulty through effortful speech production during conversation
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An investigation of Chinese postgraduate students' experiences on a data-visualized English writing feedback platform
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Dai, Kun. - : Institute of Electrical and Electronics Engineers, 2017
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Social moments: A perspective on interaction for social robotics
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Retesting the limits of data-driven learning: feedback and error correction
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Searching for My Lady’s Bonnet: discovering poetry in the National Library of Australia’s newspapers database
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An investigation of differences and changes in L2 writing anxiety between blended and conventional english language learning context
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