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Student’s Attention Improvement Supported by Physiological Measurements Analysis

Abstract : The focus of the most recent theories of emotional state analysis is the Autonomic Nervous System. Those theories propose that sympathetic and parasympathetic nervous systems interact antagonistically accordingly to each emotional state implying variations of interbeat intervals of consecutive heart beats. Emotional arousal and attention can be inferred based on the electrocardiogram (ECG) specifically through Heart Rate Variability (HRV) analysis, including the Low Frequency (LF), High Frequency (HF), and ratio LF/HF. The aim of this study is to analyze the impact of classic background music, in students’ emotional arousal and attention, and performance in the context of e-Learning training courses. As a result, it is foreseen the development of a system integrating wearables to smoothly gather the mentioned biosignals, which will be able to sense user’s emotions to further automatically propose recommendations for better learning approaches and contents, aiming student’s attention improvement.
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Andreia Artífice, Fernando Ferreira, Elsa Marcelino-Jesus, João Sarraipa, Ricardo Jardim-Gonçalves. Student’s Attention Improvement Supported by Physiological Measurements Analysis. 8th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), May 2017, Costa de Caparica, Portugal. pp.93-102, ⟨10.1007/978-3-319-56077-9_8⟩. ⟨hal-01629591⟩

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