Search Results (7)

It is important to work on educating children’s emotional intelligence, namely the re-awareness and control of emotions, both their own and those around them so that they feel empathy for others, establish and maintain positive relationships and make conscious decisions. This document describes a proposal for a gamified solution, based on the development of a multimedia product, which aims to help children, parents and teachers in the education of emotional intelligence in children. The solution makes it possible to present everyday scenarios to children, allowing adults to find out how they feel and, based on this information, to work on feelings and social behaviour. The solution was based on research into studies available in scientific databases on children’s emotional intelligence, as well as research into exercises that can help work on this same issue. Once implemented, the solution was tested with children from a primary school, where it was possible to collect feedback from them and their teachers and make improvements. This study presents the design, development and evaluation of a gamified application for children focused on emotional intelligence. The methodology used is based on a systematic literature review following the PRISMA protocol and the development of an iterative multimedia product. The study sample included around 200 elementary school children, where it was possible to collect qualitative feedback to evaluate the effectiveness of the application. The results obtained made it possible to make improvements to the design of the application and to obtain feedback from the teachers, which was very positive, but transmitted by direct interview. Full article

Social interactions are a part of our everyday lives. This can be challenging for individuals who experience social interactions as demanding, such as persons with autism spectrum disorder (ASD). Therefore, different types of training exist to help individuals affected by ASD practice in challenging situations. Digital applications offer advantages over traditional training because they can better address the individual needs of people with ASD. The development of a therapeutic application initially requires identifying appropriate emotion-relevant scenarios of social interaction. Based on a previous study evaluating text-based scenarios with different levels of complexity, a virtual reality (VR) environment was developed to assess the applicability of the scenarios in VR. Therefore, an experimental study was conducted. Two different scenarios of social interaction, each with four different levels of complexity, were presented and evaluated by 18 participants (10 males, eight females). A multidimensional approach was used to combine subjective assessments and psychophysiological measures (ECG and EDA). The results showed that the implementation of the scenarios in VR was able to differentiate between different levels of complexity. As the long-term target is to implement the findings in a therapeutic application for people with ASD, the results of the study are promising for the achievement of this goal. Full article

Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect’s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field. Full article

Bringing emotion recognition (ER) out of the controlled laboratory setup into everyday life can enable applications targeted at a broader population, e.g., helping people with psychological disorders, assisting kids with autism, monitoring the elderly, and general improvement of well-being. This work reviews progress in sensors and machine learning methods and techniques that have made it possible to move ER from the lab to the field in recent years. In particular, the commercially available sensors collecting physiological data, signal processing techniques, and deep learning architectures used to predict emotions are discussed. A survey on existing systems for recognizing emotions in real-life scenarios—their possibilities, limitations, and identified problems—is also provided. The review is concluded with a debate on what challenges need to be overcome in the domain in the near future. Full article

Music and familiar everyday sounds can be meaningful for people with dementia by providing benefits such as evoking memories and emotions or prompting social interactions with caregivers or relatives. Motivated by this potential, researchers and designers are investigating how to leverage these beneficial effects of sound in care environments through audio-based technology. However, there is a gap in the knowledge of how audio-based technology can be successfully implemented within everyday care practice. In this paper, we present the outcome of three participatory workshops with 18 professional caregivers to explore how audio-based technology can add value to existing care processes and activities in residential dementia care. During the participatory workshops, professional caregivers (1) mapped existing care activities; (2) linked findings in research with practice, and (3) designed scenarios for the Vita sound cushion. Care professionals indicate how audio-based technology can support existing care practice by influencing the mood of residents and by supporting social interaction during moments of care, daytime activities, or situational sessions. This study bridges research findings with insights from practice, contributing to a shared understanding of opportunities for embedding audio-based technology in dementia care. These opportunities motivate future research to implement and evaluate audio-based technology in residential dementia care. Full article

Emotions are embedded in the everyday life of every individual. In the same way their emotions are immersed in their cultural legacy, they are conditioned by behaviors that cannot be separated from an educational context. In this sense, physical education (PE) has been reported as a school subject that facilitates the emotional expression of primary and secondary school students. Thus, it seems to be important to understand how applied methodologies that enhance emotional education in this context help to develop students’ skills regarding emotional management. In this context, 18 articles were selected from the Web of Science (WoS) under robust inclusion and exclusion criteria, which posteriorly were categorized and deeply analyzed. Results show that PE creates a favorable scenario for the expression of emotions. However, there is not sufficient evidence regarding emotional education methodologies that can be applied by PE teachers in their classes to improve primary and secondary students’ management of emotions. It has been concluded that more proposals based on scientific evidence in this field are required. Full article

Sitting for an extended time may cause a serious chronic disease such as a musculoskeletal disorder, or a cardiovascular disease, diabetes, or obesity. Because a consistently improper posture from early childhood to adolescence can have a number of undesirable effects on the formation of the musculoskeletal structure, learning to maintain a correct posture should be emphasized. A consistently improper posture can not only cause physical problems, it may also lead to emotional issues such as distractions, an attention deficit, and hyperactivity, and the possibility of a low efficiency and performance on assignments is high when the students have a low concentration. The present study implemented a distracted estimation system based on sensor fusion through correlation analysis with concentration that could estimate the level of distraction and prevent musculoskeletal diseases caused by poor sitting posture habits in daily life. The implemented system was designed in the form of a sitting cushion to reflect the ethological movements and characteristics of a sitting position that modern people spend a large amount of time in, and can be easily applied to existing chairs. Both algorithms installed in the system detected the center of gravity of the seated person and displayed positional changes that occurred based on the intensity of the postural changes when moving; thus, simultaneous determination of posture and impulsive behavior was possible. To evaluate the system performance, a posture determination evaluation was conducted, along with distraction estimation according to the rate of changes in posture that occur in everyday life. In addition, to evaluate performance in daily life, a movie-watching scenario was set up, and the distracted-limit estimation and concentration indices according to the rate of changes in posture were comparatively evaluated by reviewing a video of the subjects. The results of the posture determination performance evaluation through 100 posture repetitions on 10 subjects showed a high detection performance of 99.04%. The Pearson’s correlation coefficient results showed a high correlation coefficient (inverse) of r = −0.975076 and a P-VALUE = $1.654\times {10}^{-6}$ . This experiment objectively confirmed the correlation between the DLE Index (based on postural change) and the CI Index (based on EEG). Full article