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This paper presents the findings of a preliminary study about the use of bio-feedback sensors in the context of an educational hypermedia environment, skin conductance, blood volume pulse and heart rate physiological data were gathered. The aim of the study was to examine learners' emotional arousal variability, and possible correlations of the physiological data with other psychological constructs...
Recording and processing physiological signals from real life for the purpose of affect detection presents many challenges beyond those encountered in the laboratory. Issues such as finding the proper baseline and normalization take on a time dependent meaning. Physical motion also becomes an important factor as these physiological signals often overwhelm those caused by affect. Motion also has an...
The ability to recognize emotion is one of the hallmarks of emotion intelligence. This paper proposed to recognize emotion using physiological signals obtained from multiple subjects. IAPS images were used to elicit target emotions. Five physiological signals: Blood volume pulse (BVP), Electromyography (EMG), Skin Conductance (SC), Skin Temperature (SKT) and Respiration (RESP) were selected to extract...
A stress detection system is developed based on the physiological signals monitored by non-invasive and non-intrusive sensors. The development of this emotion recognition system involved three stages: experiment setup for physiological sensing, signal preprocessing for the extraction of affective features and affective recognition using a learning system. Four signals: galvanic skin response (GSR),...
A stress detection system is developed based on the physiological signals monitored by non-invasive and non-intrusive sensors. The development of this emotion recognition system involved three stages: experiment setup for physiological sensing, signal preprocessing for the extraction of affective features and affective recognition using a learning system. Four signals: galvanic skin response (GSR),...
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