Filtering of Spontaneous and Low Intensity Emotions in Educational Contexts

Salmerón-Majadas, Sergio; Arevalillo‐Herráez, Miguel; Santos, Olga C.; Saneiro, Mar; Cabestrero, Raúl; Quirós, Pilar; Arnau, David; Boticario, Jesús G.

doi:10.1007/978-3-319-19773-9_43

Cited by 22 publications

(12 citation statements)

References 21 publications

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“…This is, in fact, equivalent to treating all training data for different individuals as if they belong to the same subject [28,29,30,59]. In intra-subject approaches, an independent model is built for each subject si [27,31,32,40,41], by considering only training data that belong to that particular subject (scriptTsi). The high accuracy achieved by some subject-independent models (e.g., [28,29,59]) suggests that some relations between features and emotions hold for most individuals.…”

Section: Discussionmentioning

confidence: 99%

“…In this case, a single model is built by considering all data as if it were coming from the same subject, without taking the user’s particularities into consideration. Despite the high prediction rates obtained in some cases, these can be significantly improved by using individual models adapted to each user [30,31,32]. However, subject-dependent (intra-subject) approaches suffer from two severe drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Arevalillo‐Herráez

Cobos

Roger

et al. 2019

Sensors

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Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Arevalillo‐Herráez

Cobos

Roger

et al. 2019

Sensors

Self Cite

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“…In this way, the way the teacher interacts with the environment can be drastically changed, as the environment might be able to identify the teacher's need and the reason for help by its own. In particular, affective states can be automatically detected by combining several data sources (Calvo and D'Mello 2010), which can take advantage of multimodal data mining techniques (Salmeron-Majadas et al 2015). Continuous and context-aware monitoring of human physiological parameters in a non-intrusive way has only been possible with the recent development of wearable sensors such as smart watches, bracelets, and t-shirts (Mukhopadhyay 2015).…”

Section: Future Workmentioning

confidence: 99%

Reducing techno-anxiety in high school teachers by improving their ICT problem-solving skills

Muñoz

Penalba

Sánchez

et al. 2016

Behaviour & Information Technology

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Teachers need to continuously update their information and communication technologies (ICT) knowledge, but they are usually not trained to deal with the problems arising from their use. In fact, studies in the literature report techno-anxiety (i.e. unpleasant physiological activation and discomfort due to present or future use of ICT) in teachers. Thus, the goal of this action research is to study if teachers' techno-anxiety can be reduced by increasing their ability to solve technological problems. An inter-subject experiment has been carried out with 46 teachers. High school teachers were chosen because they are digital immigrants, while at the moment of this research their students are digital natives (born around year 2000). Since we could not find any specific training for teachers to increase their resolution skills of technological problems, in order to apply the treatment for our study, we have designed and deployed an online course about ICT problem-solving skills based on the 70/20/10 model for learning and development. Results show the success of the course when it comes to increasing the ICT problem-solving skills and to reducing techno-anxiety. ARTICLE HISTORY

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“…The concept, referred to as emotional marketing [44] milk affective computing for decision support [45,46] and for product feedback assessment [47,48]. E-Learning is also setting its feet wet by exploiting emotion recognition [49,50]. An emotional state of the learner may suggest a modification in the presentation style and to be more interactive for effective tutoring.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study

Mehta

Siddiqui

Javaid

2019

Sensors

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Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human–machine interaction where the facial emotion recognition and the intensity of emotion play vital roles. The existing works usually do not encode the intensity of the observed facial emotion and even less involve modeling the multi-class facial behavior data jointly. Our work involves recognizing the emotion along with the respective intensities of those emotions. The algorithms used in this comparative study are Gabor filters, a Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction. For classification, we have used Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN). This attains emotion recognition and intensity estimation of each recognized emotion. This is a comparative study of classifiers used for facial emotion recognition along with the intensity estimation of those emotions for databases. The results verified that the comparative study could be further used in real-time behavioral facial emotion and intensity of emotion recognition.

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Filtering of Spontaneous and Low Intensity Emotions in Educational Contexts

Cited by 22 publications

References 21 publications

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Reducing techno-anxiety in high school teachers by improving their ICT problem-solving skills

Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study

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