2024
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Guillen-Sanz, Henar; Checa, David; Miguel-Alonso, Inés; Bustillo, Andrés A systematic review of wearable biosensor usage in immersive virtual reality experiences Journal Article In: Virtual Reality, vol. 28, no. 74, 2024, ISSN: 1434-9957. @article{guillen-sanz2024,
title = {A systematic review of wearable biosensor usage in immersive virtual reality experiences},
author = {Henar Guillen-Sanz and David Checa and Inés Miguel-Alonso and Andrés Bustillo},
url = {https://rdcu.be/dAEEn},
doi = {10.1007/s10055-024-00970-9},
issn = {1434-9957},
year = {2024},
date = {2024-03-08},
urldate = {2024-03-08},
journal = {Virtual Reality},
volume = {28},
number = {74},
abstract = {Wearable biosensors are increasingly incorporated in immersive Virtual Reality (iVR) applications. A trend that is attributed to the availability of better quality, less costly, and easier-to-use devices. However, consensus is yet to emerge over the most optimal combinations. In this review, the aim is to clarify the best examples of biosensor usage in combination with iVR applications. The high number of papers in the review (560) were classified into the following seven fields of application: psychology, medicine, sports, education, ergonomics, military, and tourism and marketing. The use of each type of wearable biosensor and Head-Mounted Display was analyzed for each field of application. Then, the development of the iVR application is analyzed according to its goals, user interaction levels, and the possibility of adapting the iVR environment to biosensor feedback. Finally, the evaluation of the iVR experience was studied, considering such issues as sample size, the presence of a control group, and post-assessment routines. A working method through which the most common solutions, the best practices, and the most promising trends in biofeedback-based iVR applications were identified for each field of application. Besides, guidelines oriented towards good practice are proposed for the development of future iVR with biofeedback applications. The results of this review suggest that the use of biosensors within iVR environments need to be standardized in some fields of application, especially when considering the adaptation of the iVR experience to real-time biosignals to improve user performance.},
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pubstate = {published},
tppubtype = {article}
}
Wearable biosensors are increasingly incorporated in immersive Virtual Reality (iVR) applications. A trend that is attributed to the availability of better quality, less costly, and easier-to-use devices. However, consensus is yet to emerge over the most optimal combinations. In this review, the aim is to clarify the best examples of biosensor usage in combination with iVR applications. The high number of papers in the review (560) were classified into the following seven fields of application: psychology, medicine, sports, education, ergonomics, military, and tourism and marketing. The use of each type of wearable biosensor and Head-Mounted Display was analyzed for each field of application. Then, the development of the iVR application is analyzed according to its goals, user interaction levels, and the possibility of adapting the iVR environment to biosensor feedback. Finally, the evaluation of the iVR experience was studied, considering such issues as sample size, the presence of a control group, and post-assessment routines. A working method through which the most common solutions, the best practices, and the most promising trends in biofeedback-based iVR applications were identified for each field of application. Besides, guidelines oriented towards good practice are proposed for the development of future iVR with biofeedback applications. The results of this review suggest that the use of biosensors within iVR environments need to be standardized in some fields of application, especially when considering the adaptation of the iVR experience to real-time biosignals to improve user performance. |
Garrido-Labrador, José Luis; Serrano-Mamolar, Ana; Maudes-Raedo, Jesús; Rodríguez, Juan José; García-Osorio, César Ensemble methods and semi-supervised learning for information fusion: A review and future research directions Journal Article In: Information Fusion, vol. 107, 2024. @article{garrido2024ensemble,
title = {Ensemble methods and semi-supervised learning for information fusion: A review and future research directions},
author = {José Luis Garrido-Labrador and Ana Serrano-Mamolar and Jesús Maudes-Raedo and Juan José Rodríguez and César García-Osorio},
url = {https://doi.org/10.1016/j.inffus.2024.102310},
doi = {10.1016/j.inffus.2024.102310},
year = {2024},
date = {2024-02-02},
urldate = {2024-02-02},
journal = {Information Fusion},
volume = {107},
abstract = {Advances over the past decade at the intersection of information fusion methods and Semi-Supervised Learning (SSL) are investigated in this paper that grapple with challenges related to limited labelled data. To do so, a bibliographic review of papers published since 2013 is presented, in which ensemble methods are combined with new machine learning algorithms. A total of 128 new proposals using SSL algorithms for ensemble construction are identified and classified. All the methods are categorised by approach, ensemble type, and base classifier. Experimental protocols, pre-processing, dataset usage, unlabelled ratios, and statistical tests are also assessed, underlining the major trends, and some shortcomings of particular studies. It is evident from this literature review that foundational algorithms such as self-training and co-training are influencing current developments, and that innovative ensemble …
},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Advances over the past decade at the intersection of information fusion methods and Semi-Supervised Learning (SSL) are investigated in this paper that grapple with challenges related to limited labelled data. To do so, a bibliographic review of papers published since 2013 is presented, in which ensemble methods are combined with new machine learning algorithms. A total of 128 new proposals using SSL algorithms for ensemble construction are identified and classified. All the methods are categorised by approach, ensemble type, and base classifier. Experimental protocols, pre-processing, dataset usage, unlabelled ratios, and statistical tests are also assessed, underlining the major trends, and some shortcomings of particular studies. It is evident from this literature review that foundational algorithms such as self-training and co-training are influencing current developments, and that innovative ensemble …
|
Miguel-Alonso, Ines; Checa, David; Guillen-Sanz, Henar; Bustillo, Andres Evaluation of the novelty effect in immersive Virtual Reality learning experiences Journal Article In: Virtual Reality, vol. 28, no. 27, 2024, ISSN: 1434-9957. @article{miguel-alonso2024,
title = {Evaluation of the novelty effect in immersive Virtual Reality learning experiences},
author = {Ines Miguel-Alonso and David Checa and Henar Guillen-Sanz and Andres Bustillo},
url = {https://doi.org/10.1007/s10055-023-00926-5 },
doi = {https://doi.org/10.1007/s10055-023-00926-5 },
issn = {1434-9957},
year = {2024},
date = {2024-01-21},
urldate = {2024-01-21},
journal = {Virtual Reality},
volume = {28},
number = {27},
abstract = {In this study, the novelty effect or initial fascination with new technology is addressed in the context of an immersive Virtual Reality (iVR) experience. The novelty effect is a significant factor contributing to low learning outcomes during initial VR learning experiences. The aim of this research is to measure the effectiveness of a tutorial at mitigating the novelty effect of iVR learning environments among first-year undergraduate students. The iVR tutorial forms part of the iVR learning experience that involves the assembly of a personal computer, while learning the functions of the main components. 86 students participated in the study, divided into a Control group (without access to the tutorial) and a Treatment group (completing the tutorial). Both groups showed a clear bimodal distribution in previous knowledge, due to previous experience with learning topics, giving us an opportunity to compare tutorial effects with students of different backgrounds. Pre- and post-test questionnaires were used to evaluate the experience. The analysis included such factors as previous knowledge, usability, satisfaction, and learning outcomes categorized into remembering, understanding, and evaluation. The results demonstrated that the tutorial significantly increased overall satisfaction, reduced the learning time required for iVR mechanics, and improved levels of student understanding, and evaluation knowledge. Furthermore, the tutorial helped to homogenize group behavior, particularly benefiting students with less previous experience in the learning topic. However, it was noted that a small number of students still received low marks after the iVR experience, suggesting potential avenues for future research.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this study, the novelty effect or initial fascination with new technology is addressed in the context of an immersive Virtual Reality (iVR) experience. The novelty effect is a significant factor contributing to low learning outcomes during initial VR learning experiences. The aim of this research is to measure the effectiveness of a tutorial at mitigating the novelty effect of iVR learning environments among first-year undergraduate students. The iVR tutorial forms part of the iVR learning experience that involves the assembly of a personal computer, while learning the functions of the main components. 86 students participated in the study, divided into a Control group (without access to the tutorial) and a Treatment group (completing the tutorial). Both groups showed a clear bimodal distribution in previous knowledge, due to previous experience with learning topics, giving us an opportunity to compare tutorial effects with students of different backgrounds. Pre- and post-test questionnaires were used to evaluate the experience. The analysis included such factors as previous knowledge, usability, satisfaction, and learning outcomes categorized into remembering, understanding, and evaluation. The results demonstrated that the tutorial significantly increased overall satisfaction, reduced the learning time required for iVR mechanics, and improved levels of student understanding, and evaluation knowledge. Furthermore, the tutorial helped to homogenize group behavior, particularly benefiting students with less previous experience in the learning topic. However, it was noted that a small number of students still received low marks after the iVR experience, suggesting potential avenues for future research. |
Martinez, Kim; Checa, David; Bustillo, Andres Development of the Engagement Playability and User eXperience (EPUX) Metric for 2D-Screen and VR Serious Games: A Case-Study Validation of Hellblade: Senua’s Sacrifice Journal Article In: Electronics, vol. 13, iss. 281, no. 2, pp. 281, 2024. @article{martinez2024,
title = {Development of the Engagement Playability and User eXperience (EPUX) Metric for 2D-Screen and VR Serious Games: A Case-Study Validation of Hellblade: Senua’s Sacrifice },
author = {Kim Martinez and David Checa and Andres Bustillo},
url = {https://doi.org/10.3390/electronics13020281},
doi = {electronics13020281},
year = {2024},
date = {2024-01-08},
urldate = {2024-01-08},
journal = {Electronics},
volume = {13},
number = {2},
issue = {281},
pages = {281},
abstract = {Research into the design of serious games still lacks metrics to evaluate engagement with the experience so that users can achieve the learning aims. This study presents the new EPUX metric, based on playability and User eXperience (UX) elements, to measure the capability of any serious game to maintain the attention of players. The metric includes (1) playability aspects: game items that affect the emotions of users and that constitute the different layers of the game, i.e., mechanics, dynamics and aesthetics; and (2) UX features: motivation, meaningful choices, usability, aesthetics and balance both in the short and in the long term. The metric is also adapted to evaluate virtual reality serious games (VR-SGs), so that changes may be considered to features linked to playability and UX. The case study for the assessment of the EPUX metric is Hellblade, developed in two versions: one for 2D-screens and the other for VR devices. The comparison of the EPUX metric scores for both versions showed that (1) some VR dynamics augmented the impact of gameplay and, in consequence, engagement capacity; and (2) some game design flaws were linked to much lower scores. Among those flaws were low numbers of levels, missions, and items; no tutorial to enhance usability; and lack of strategies and rewards to increase motivation in the long term.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Research into the design of serious games still lacks metrics to evaluate engagement with the experience so that users can achieve the learning aims. This study presents the new EPUX metric, based on playability and User eXperience (UX) elements, to measure the capability of any serious game to maintain the attention of players. The metric includes (1) playability aspects: game items that affect the emotions of users and that constitute the different layers of the game, i.e., mechanics, dynamics and aesthetics; and (2) UX features: motivation, meaningful choices, usability, aesthetics and balance both in the short and in the long term. The metric is also adapted to evaluate virtual reality serious games (VR-SGs), so that changes may be considered to features linked to playability and UX. The case study for the assessment of the EPUX metric is Hellblade, developed in two versions: one for 2D-screens and the other for VR devices. The comparison of the EPUX metric scores for both versions showed that (1) some VR dynamics augmented the impact of gameplay and, in consequence, engagement capacity; and (2) some game design flaws were linked to much lower scores. Among those flaws were low numbers of levels, missions, and items; no tutorial to enhance usability; and lack of strategies and rewards to increase motivation in the long term. |
Barbero-Aparicio, José A.; Olivares-Gil, Alicia; Rodríguez, Juan J.; García-Osorio, César; Díez-Pastor, José F. Addressing data scarcity in protein fitness landscape analysis: A study on semi-supervised and deep transfer learning techniques Journal Article In: Information Fusion, vol. 102, pp. 102035, 2024, ISSN: 1566-2535. @article{barbero-aparicio2023b,
title = {Addressing data scarcity in protein fitness landscape analysis: A study on semi-supervised and deep transfer learning techniques},
author = {José A. Barbero-Aparicio and Alicia Olivares-Gil and Juan J. Rodríguez and César García-Osorio and José F. Díez-Pastor},
url = {https://www.sciencedirect.com/science/article/pii/S1566253523003512},
doi = {10.1016/j.inffus.2023.102035},
issn = {1566-2535},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Information Fusion},
volume = {102},
pages = {102035},
abstract = {This paper presents a comprehensive analysis of deep transfer learning methods, supervised methods, and semi-supervised methods in the context of protein fitness prediction, with a focus on small datasets. The analysis includes the exploration of the combination of different data sources to enhance the performance of the models. While deep learning and deep transfer learning methods have shown remarkable performance
in situations with abundant data, this study aims to address the more realistic scenario faced by wet lab researchers, where labeled data is often limited. The novelty of this work lies in its examination of deep transfer learning in the context of small datasets and its consideration of semi-supervised methods and multi-view strategies. While previous research has extensively explored deep transfer learning in large dataset scenarios, little attention has been given to its efficacy in small dataset settings or its comparison with semi-supervised approaches. Our findings suggest that deep transfer learning, exemplified by ProteinBERT, shows promising performance in this context compared to the rest of the methods across various evaluation metrics, not only in small dataset contexts but also in large dataset scenarios. This highlights the robustness and versatility of deep transfer learning in protein fitness prediction tasks, even with limited labeled data. The results of this study shed light on the potential of deep transfer learning as a state-of-the-art approach in the field of protein fitness prediction. By leveraging pre-trained models and fine-tuning them on small datasets, researchers can achieve competitive performance surpassing traditional supervised and semi-supervised methods. These findings provide valuable insights for wet lab researchers who face the challenge of limited labeled data, enabling them to make informed decisions when selecting the most effective methodology for their specific protein fitness prediction tasks. Additionally, the study investigated the combination of two different sources of information (encodings) through our enhanced semi-supervised methods, yielding noteworthy results improving their base model and providing valuable insights for further research. The presented analysis contributes to a better understanding of the capabilities and limitations of different learning approaches in small dataset scenarios, ultimately aiding in the development of improved protein fitness prediction methods},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents a comprehensive analysis of deep transfer learning methods, supervised methods, and semi-supervised methods in the context of protein fitness prediction, with a focus on small datasets. The analysis includes the exploration of the combination of different data sources to enhance the performance of the models. While deep learning and deep transfer learning methods have shown remarkable performance
in situations with abundant data, this study aims to address the more realistic scenario faced by wet lab researchers, where labeled data is often limited. The novelty of this work lies in its examination of deep transfer learning in the context of small datasets and its consideration of semi-supervised methods and multi-view strategies. While previous research has extensively explored deep transfer learning in large dataset scenarios, little attention has been given to its efficacy in small dataset settings or its comparison with semi-supervised approaches. Our findings suggest that deep transfer learning, exemplified by ProteinBERT, shows promising performance in this context compared to the rest of the methods across various evaluation metrics, not only in small dataset contexts but also in large dataset scenarios. This highlights the robustness and versatility of deep transfer learning in protein fitness prediction tasks, even with limited labeled data. The results of this study shed light on the potential of deep transfer learning as a state-of-the-art approach in the field of protein fitness prediction. By leveraging pre-trained models and fine-tuning them on small datasets, researchers can achieve competitive performance surpassing traditional supervised and semi-supervised methods. These findings provide valuable insights for wet lab researchers who face the challenge of limited labeled data, enabling them to make informed decisions when selecting the most effective methodology for their specific protein fitness prediction tasks. Additionally, the study investigated the combination of two different sources of information (encodings) through our enhanced semi-supervised methods, yielding noteworthy results improving their base model and providing valuable insights for further research. The presented analysis contributes to a better understanding of the capabilities and limitations of different learning approaches in small dataset scenarios, ultimately aiding in the development of improved protein fitness prediction methods |