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Detection of Stress Stimuli in Learning Contexts of iVR Environments

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Extended Reality (XR Salento 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14219))

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Abstract

The use of eye-tracking in immersive Virtual Reality (iVR) is becoming an important tool for improving the learning outcomes. Nevertheless, the best Machine Learning (ML) technologies for the exploitation of eye-tracking data is yet unclear. Actually, one of the main drawbacks of some ML technologies, such as classifiers, is the scarce labeled data for training models, being the process of data annotation time-consuming and expensive. This paper presents a complete experimentation where different ML algorithms were tested, both supervised and semi-supervised, for trying to identify the stressors/distractors present in iVR learning experiences simulating the operation of a bridge crane. Results shown that the use of semi-supervised techniques can improve the performance of the Machine Learning methods making possible the identification of stressful situations in iVR environments. The use of semi-supervised learning techniques makes possible training ML algorithms without the need of great amount of labeled data which makes the data exploitation cheaper and easier.

This work was supported by the Junta de Castilla y León under project BU055P20 (JCyL/FEDER, UE), the Ministry of Science and Innovation of Spain under project PID2020-119894GB-I00, co-financed through European Union FEDER funds. This work is part of the project Humanaid (TED2021-129485B-C43) funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR. We also acknowledge European Union NextGenerationEU/PRTR funds for the Margarita Salas 2022–2024 Grant awarded by Universidad de Burgos. It also was supported through the Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant (EDU/875/2021).

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Notes

  1. 1.

    Broadly speaking, in ML community an ensemble is an algorithm (meta-model) that is composed by several algorithms (base models/estimators) that work together [14]. The decisions made by an ensemble depend on the predictions of the base models.

  2. 2.

    The source code for the Machine Learning experimentation can be publicly accessed from the following link: https://github.com/Josemi/StressDetection_iVR.

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Authors and Affiliations

  1. Universidad de Burgos, 09001, Burgos, Spain

    José Miguel Ramírez-Sanz, Helia Marina Peña-Alonso, Ana Serrano-Mamolar, Álvar Arnaiz-González & Andrés Bustillo

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  1. José Miguel Ramírez-Sanz
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  2. Helia Marina Peña-Alonso
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  3. Ana Serrano-Mamolar
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  4. Álvar Arnaiz-González
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  5. Andrés Bustillo
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Correspondence to Álvar Arnaiz-González .

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Editors and Affiliations

  1. University of Salento, Lecce, Italy

    Lucio Tommaso De Paolis

  2. University of Naples Federico II, Naples, Italy

    Pasquale Arpaia

  3. CNR-STIIMA, Lecco, Italy

    Marco Sacco

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Ramírez-Sanz, J.M., Peña-Alonso, H.M., Serrano-Mamolar, A., Arnaiz-González, Á., Bustillo, A. (2023). Detection of Stress Stimuli in Learning Contexts of iVR Environments. In: De Paolis, L.T., Arpaia, P., Sacco, M. (eds) Extended Reality. XR Salento 2023. Lecture Notes in Computer Science, vol 14219. Springer, Cham. https://doi.org/10.1007/978-3-031-43404-4_29

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  • DOI: https://doi.org/10.1007/978-3-031-43404-4_29

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