TY - GEN
T1 - Student’s attention improvement supported by physiological measurements analysis
AU - Artífice, Andreia
AU - Ferreira, Fernando
AU - Marcelino-Jesus, Elsa
AU - Sarraipa, João
AU - Jardim-Gonçalves, Ricardo
N1 - sem pdf conforme despacho.
ERASMUS+: Higher Education International Capacity Building - ACACIA - 561754-EPP-1-2015-1-CO-EPKA2-CBHE-JP ;
Horizon - AquaSmart - Aquaculture Smart and Open Data Analytics as a Service - 644715
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Attention
KW - eLearning
KW - Emotional arousal
KW - Heart rate variability
UR - http://www.scopus.com/inward/record.url?scp=85018160066&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-56077-9_8
DO - 10.1007/978-3-319-56077-9_8
M3 - Conference contribution
AN - SCOPUS:85018160066
SN - 9783319560762
VL - 499
T3 - IFIP Advances in Information and Communication Technology
SP - 93
EP - 102
BT - Technological Innovation for Smart Systems - 8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2017, Proceedings
PB - Springer New York LLC
T2 - 8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2017
Y2 - 3 May 2017 through 5 May 2017
ER -