Identification of Emotional States and Their Potential

Francisti, Jan; Balogh, Zoltán

doi:10.1007/978-981-13-6861-5_58

Cited by 3 publications

(1 citation statement)

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“…The Internet of Things allows objects to be captured and controlled remotely over existing network infrastructures, creating opportunities for further direct integration of the physical world into computer systems, resulting in increased efficiency, accuracy and economic benefits [1]. IoT is augmented by sensors and actuators, making it a cyber-physical system that includes technologies such as smart grids, smart homes, intelligent transport, smart cities, and also smart education [2,3].…”

Section: Introductionmentioning

confidence: 99%

Application Experiences Using IoT Devices in Education

et al. 2020

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The Internet of Things (IoT) is becoming a regular part of our lives. The devices can be used in many sectors, such as education and in the learning process. The article describes the possibilities of using commonly available devices such as smart wristbands (watches) and eye tracking technology, i.e., using existing technical solutions and methods that rely on the application of sensors while maintaining non-invasiveness. By comparing the data from these devices, we observed how the students’ attention affects their results. We looked for a correlation between eye tracking, heart rate, and student attention and how it all impacts their learning outcomes. We evaluate the obtained data in order to determine whether there is a degree of dependence between concentration and heart rate of students.

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Section: Introductionmentioning

confidence: 99%

Application Experiences Using IoT Devices in Education

et al. 2020

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Automatic detection and classification of emotional states in virtual reality and standard environments (LCD): comparing valence and arousal of induced emotions

et al. 2021

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The following case study was carried out on a sample of one experimental and one control group. The participants of the experimental group watched the movie section from the standardized LATEMO-E database via virtual reality (VR) on Oculus Rift S and HTC Vive Pro devices. In the control group, the movie section was displayed on the LCD monitor. The movie section was categorized according to Ekman's and Russell's classification model of evoking an emotional state. The range of valence and arousal was determined in both observed groups. Valence and arousal were measured in each group using a Self-Assessment Manikin (SAM). The control group was captured by a camera and evaluated by Affdex software from Affectiva in order to compare valence values. The control group showed a very high correlation (0.92) between SAM and Affdex results. Having considered the Affdex results as a reference value, it can be concluded that SAM participants evaluated their emotions objectively. The results from both groups show that the movie section is supposed to evoke negative emotion. Negative emotion was perceived more intensely than its counterpart, positive emotion. Using virtual reality to evoke negative emotion (anger) has confirmed that VR triggers a significantly stronger intensity of emotion than LCD.

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