Publications

@article{Miguel-Alonso2025,

title = {Identifying users of immersive virtual-reality serious games through machine-learning techniques},

author = {Ines Miguel-Alonso and Juan J. Rodríguez and Ana Serrano-Mamolar and Andres Bustillo},

doi = {10.1007/s10055-025-01232-y},

issn = {1434-9957},

year  = {2025},

date = {2025-12-00},

urldate = {2025-12-00},

journal = {Virtual Reality},

volume = {29},

number = {4},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title>

          <jats:p>User identification is currently an open issue in immersive Virtual Reality (iVR) environments. Three main goals are usually associated with the use of tracking-data and Machine-Learning (ML) techniques: safeguarding privacy, user authentication, and user-experience customization. However, research to date has only involved very limited recordings of user data (<jats:italic>e</jats:italic>.<jats:italic>g</jats:italic>., on a single session and for low-interactive situations), rare in real iVR environments. So, the research gap between real iVR data and ML techniques for user identification is addressed in this paper. To do so, a 3-session iVR experience of operating a bridge crane is considered. In this simple yet highly interactive learning action, the dataset records of user performance show rapid changes between one experience and another. Eye, head, and hand movements of 64 users of similar age and with comparable previous experience were all recorded while engaged with the experience. The final raw dataset had a size of approximately 50 M data points with 25 attributes that were mainly temporal series values. Secondly, different ML algorithms were used for user identification: Decision Tree, Random Forest, XGBoost, k-Nearest Neighbors, Support Vector Machines, and Multilayer Perceptron. The results showed that ML ensemble learning techniques, particularly Random Forest, were the most suitable solutions on the basis of different measures for the prediction of user identity. Additionally, the inclusion of stress and no-stress conditions significantly enhanced model performance, highlighting the importance of data diversity. Temporal segmentation revealed that user identification during later phases of the exercise was slightly more effective, due to increased individual variability. Finally, a minimum duration of the iVR experience was identified as a requirement to assure high identification rates.</jats:p>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{maestro-prieto2025,

title = {Semi-supervised techniques to address the scarcity of experimental data: a case study of single point incremental forming},

author = {José Alberto Maestro-Prieto and Pablo E. Romero and José Miguel Ramírez Sanz},

url = {https://doi.org/10.1007/s10845-025-02704-3},

doi = {10.1007/s10845-025-02704-3},

issn = {0956-5515},

year  = {2025},

date = {2025-10-31},

urldate = {2025-10-31},

journal = { Journal of Intelligent Manufacturing},

abstract = {A lack of experimental data can be especially critical in new manufacturing processes. Although experimental datasets for industrial processes are reported in various research works, their lack of homogeneity complicates any fitting with conventional numerical models. Artificial Intelligence (AI) models can be an optimal alternative to extract useful information from those unconnected datasets, while generating models that can help explain the hidden patterns within datasets and interpret the predictions of the model for final users. Moreover, an AI algorithm that could be trained with limited labeled datasets would be in high demand, as it could effectively lower implementation costs. Semi-Supervised Learning (SSL) techniques might therefore be a promising solution to respond to industrial demand for the analysis of manufacturing processes. In this research, the use of SSL techniques is proposed in a case study of surface quality prediction in single point incremental forming, a promising new manufacturing technique. Datasets were extracted from the existing bibliography to generate a 234-instance dataset with 4 different industrial specifications of roughness. The best results were obtained using a semi-supervised Co-Training algorithm. Semi-supervised methods systematically improved the results obtained with the reference supervised methods, although statistical significance has not been mainly achieved due to the limited dataset size. The results obtained with the unbalanced dataset were very promising for its industrial implementation with an extended training dataset optimized for the range of process conditions of each end-user.},

keywords = {mulitiple data source, Semi-supervised learning, single point incremental forming, SPIF, surface roughness},

pubstate = {published},

tppubtype = {article}

}

@article{maestro-prieto2025b,

title = {Semi-supervised tapping wear detection in nodular cast-iron workpieces under real industrial condition},

author = {José Alberto Maestro-Prieto and Alain Gil-Del-Val and Andrés Bustillo},

url = {https://link.springer.com/article/10.1007/s00170-025-16491-x},

doi = {10.1007/s00170-025-16491-x},

issn = {0268-3768},

year  = {2025},

date = {2025-09-19},

urldate = {2025-09-19},

journal = {International Journal of Advanced Manufacturing Technology },

abstract = {The tapping of metal components is a manufacturing task with great potential for automation, because the conditions affecting the industrial components are of limited variability. However, automation encounters two main problems: both the human- and the time-related costs associated with the manual classification of threads are excessive, and thread quality can vary greatly, due to tapping tool wear. In this study, the use of semi-supervised algorithms is proposed to improve the performance of machine learning–based models trained on real industrial datasets. The strategy was validated on a dataset of more than 7000 threads produced with 36 different tapping tools under the same working conditions involving nodular cast iron workpieces. Several algorithms were trained using datasets with different features and data processing. The best results were obtained with datasets using linear regression in which sinusoidal fluctuations in the raw signals were replaced by linear regressions and the slope of an 11-element rolling window was applied to extend the raw dataset. Algorithms were trained with different percentages of labeled datasets. The co-training-based algorithms almost systematically obtained the best results, yielding better results than the reference algorithms using a 100% labeled dataset. Besides, the proposed solution also achieved higher performance with 50% of labeled instances in the training dataset, drastically reducing the costs of manual labeling for that sort of industrial dataset.},

keywords = {fault detection, Semi-supervised learning, tapping, Wear},

pubstate = {published},

tppubtype = {article}

}

@article{guillen2025,

title = {Can Immersive Virtual Reality Environments Improve Stress Reduction? Experimental Design with Progressive Muscle Relaxation Training},

author = {Henar Guillen-Sanz and María del Camino Escolar-Llamazares and Itziar Quevedo-Bayona and María Ángeles Martínez-Martín and Andrés Bustillo},

editor = {IEEE},

url = {https://ieeexplore.ieee.org/document/11034975},

doi = {10.1109/ACCESS.2025.3579493},

issn = {2169-3536 },

year  = {2025},

date = {2025-06-13},

urldate = {2025-06-13},

journal = {IEEE Access},

abstract = {Psychological relaxation techniques are now fundamental in stress-management and anxiety-disorder prevention training. Progressive Muscle Relaxation (PMR) stands out among various other training programmes. However, some limitations restrict its widespread usage, such as the requirements for a therapist to be in attendance and for patients to close their eyes during treatment. In such cases, support through immersive Virtual Reality (iVR) during the training procedure may be a suitable solution. In this study, an iVR application was developed for individuals undergoing PMR training, and an experimental design with both independent and subjective measures was conducted to compare this novel approach with conventional PMR training. The study was validated in two population groups: nursing undergraduates (one training session, n=63) and undergraduates following a test anxiety programme (complete training procedure: 7 sessions, n=13). The results pointed to high satisfaction and relaxation levels across all groups. No significant differences were found between the two methodologies, suggesting that the iVR application could be a useful tool in both educational and clinical contexts. In the long experience group (7 sessions), the iVR students showed higher interest which may have contributed to adherence to the entire training procedure. Furthermore, the iVR tool demonstrated potential suitability users unable to follow conventional procedures, exemplified by a student who, due to her own anxiety-related symptoms, felt very uncomfortable when instructed to close her eyes during the relaxation training.},

keywords = {Anxiety disorders, Jacobian matrices, Legged locomotion, mental health, Muscles, Training, Virtual environments},

pubstate = {published},

tppubtype = {article}

}

@article{martineez-sanllorente2025,

title = {InvIPM: Toolbox for segmentation optimization of images of metallic objects using illumination-invariant transforms},

author = {Jonás Martínez-Sanllorente and Carlos López-Nozal and Pedro Latorre-Carmona and Raúl Marticorena-Sánchez},

editor = {ELSEVIER},

url = {https://www.sciencedirect.com/science/article/pii/S2352711025001669?via%3Dihub},

doi = {10.1016/J.SOFTX.2025.102199},

issn = {2352-7110},

year  = {2025},

date = {2025-06-02},

urldate = {2025-06-02},

journal = {SoftwareX},

volume = {31},

pages = {102199},

abstract = {The automation of industrial quality control based on artificial (computer) vision can avoid some of the problems associated with tedious and repetitive manual procedures that will often originate operator errors. Automatic quality control can also be applied uninterruptedly. However, strategies of that sort have some drawbacks. One is associated with image acquisition under controlled illumination conditions. The material characteristics of an object for analysis will also influence the final result. For example, the illumination of metallic objects or objects with metallic finishes will generate specular reflection and shadow, which must be minimized. The illumination effect on subsequent processing stages may be analysed by applying segmentation techniques (based, for instance, on clustering strategies), to identify the number of objects. In this study, a MATLAB desktop application for image processing was developed, where illumination-invariant transforms were applied prior to image segmentation, to improve the quality of segmentation results. A set of illumination-invariant transforms and clustering-based segmentation methods were applied and the segmentation quality (if there was a groundtruth image) was quantified. The experimental results obtained with 4 illumination-invariant algorithms, 4 clustering-based segmentation algorithms, and 29 images of metal parts acquired by factory operators and manually segmented by researchers, demonstrated significant improvement to image segmentation following the application of illumination-invariant transforms.},

keywords = {Illumination invariants, Image processing, Image segmentation, Industrial manufacturing, Metallic objects, Specular reflection},

pubstate = {published},

tppubtype = {article}

}

@article{rodriguez-garcia2025,

title = {LoDCalculator: A level of detail classification software for 3D models in the Blender environment},

author = {Bruno Rodriguez-Garcia and Ines Miguel-Alonso and Henar Guillen-Sanz and Andres Bustillo},

url = {https://doi.org/10.1016/j.softx.2025.102107},

doi = {10.1016/j.softx.2025.102107},

issn = {2352-7110},

year  = {2025},

date = {2025-02-19},

urldate = {2025-02-19},

journal = {SoftwareX},

volume = {30},

pages = {102107},

abstract = {The use of Level of Detail (LoD), a crucial technique in the development of 3D models, implies lower cost graphics and resource economies. These savings are evident in contexts where technical resources are limited, such as immersive Virtual Reality and whenever LoD is critical for accurate representation, such as Cultural Heritage dissemination. Consequently, various systems are used to classify 3D models based on their LoD. However, those systems have several shortcomings that hinder their widespread use. In this research, LoDCalculator, an add-on for Blender open-source modelling software, is presented to address such shortcomings. LoDCalculator ensures unambiguous, universal, and accessible classification of 3D models. It was tested by classifying 12 3D models. The scores were then compared with the evaluations of a group of students and professional 3D modelers in a subjective evaluation. The results of the comparison were satisfactory, showing minimal significant differences between the software and the evaluation group classifications.},

keywords = {3D modelling, Level of detail, Model classification, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Garrido-Labrador2025,

title = {SSLearn: A Semi-Supervised Learning library for Python},

author = {José L. Garrido-Labrador and Jesús M. Maudes-Raedo and Juan J. Rodríguez and César I. García-Osorio},

doi = {10.1016/j.softx.2024.102024},

issn = {2352-7110},

year  = {2025},

date = {2025-02-00},

urldate = {2025-02-00},

journal = {SoftwareX},

volume = {29},

publisher = {Elsevier BV},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{marticorena2025,

title = {Unveiling the Differences in Early Performance Prediction Between Online Social Sciences and STEM Courses Using Educational Data Mining},

author = {Raúl Marticorena-Sánchez and Antonio Canepa-Oneto and Carlos López-Nozal and José A. Barbero-Aparicio},

editor = {Wiley},

url = {https://doi.org/10.1111/exsy.13837

https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13837},

doi = {10.1111/exsy.13837},

year  = {2025},

date = {2025-01-20},

journal = {Expert Systems},

volume = {42},

number = {3},

pages = {e13837},

abstract = {Educational Data Mining and Learning Analytics in virtual environments can be used to diagnose student performance problems at an early stage. Information that is useful for guiding the decisions of teachers managing academic training, so that students can successfully complete their course. However, student interaction patterns may vary depending on the knowledge domain. Our aim is to design a framework applicable to online Social Sciences and STEM courses, recommending methods for building accurate early performance prediction models. A large-scale comparative study of the accuracy of multiple classifiers applied to classify the interaction logs of 32,593 students from 9 Social Sciences and 13 STEM courses is presented. Corroborating the results of other works, it was observed that high early performance prediction accuracy was obtained based on nothing other than student logs: accuracies of 0.75 in the 10th week, 0.80 in the 20th week, 0.85 in the 30th week and 0.90 in the 40th week. However, accuracy rates were observed to vary significantly, in relation to the classification algorithm and the knowledge domain (Social Sciences vs. STEM). These predictions are generally less accurate for Social Sciences compared to STEM courses, especially at the beginning of the course, with fewer differences observed in the final weeks. Additionally, this research identifies instances of low-accuracy outliers in the prediction of Social Sciences courses over time. These findings highlight the complex challenges and variations in early performance prediction across different domains in online education.},

keywords = {Educational data mining, Learning analytics, Learning at scale, Online learning logs, Student performance prediction, Supervised data-mining},

pubstate = {published},

tppubtype = {article}

}

@article{Guillen-Sanz2025,

title = {Can Immersive Virtual Reality Environments Improve Stress Reduction? Experimental Design With Progressive Muscle Relaxation Training},

author = {H. Guillen-Sanz and M. C. Escolar-Llamazares and I. Quevedo Bayona and M. A. Martínez-Martín and A. Bustillo},

doi = {10.1109/access.2025.3579493},

issn = {2169-3536},

year  = {2025},

date = {2025-01-13},

urldate = {2025-00-00},

journal = {IEEE Access},

volume = {13},

pages = {104312--104329},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{Garrido-Labrador2024,

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 J. Rodríguez and César García-Osorio},

doi = {10.1016/j.inffus.2024.102310},

issn = {1566-2535},

year  = {2024},

date = {2024-07-00},

urldate = {2024-07-00},

journal = {Information Fusion},

volume = {107},

publisher = {Elsevier BV},

keywords = {p_humanaid, PID2020-119894GB-I00, SSL},

pubstate = {published},

tppubtype = {article}

}

@article{Guillen-Sanz2024b,

title = {A systematic review of wearable biosensor usage in immersive virtual reality experiences},

author = {Henar Guillen-Sanz and David Checa and Ines Miguel-Alonso and Andres Bustillo},

doi = {10.1007/s10055-024-00970-9},

issn = {1434-9957},

year  = {2024},

date = {2024-06-00},

journal = {Virtual Reality},

volume = {28},

number = {2},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractWearable 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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Guillen-Sanz2024c,

title = {A systematic review of wearable biosensor usage in immersive virtual reality experiences},

author = {Henar Guillen-Sanz and David Checa and Ines Miguel-Alonso and Andres Bustillo},

doi = {10.1007/s10055-024-00970-9},

issn = {1434-9957},

year  = {2024},

date = {2024-06-00},

urldate = {2024-06-00},

journal = {Virtual Reality},

volume = {28},

number = {2},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>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.</jats:p>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{rodriguez-garcia2024,

title = {A systematic review of virtual 3D reconstructions of Cultural Heritage in immersive Virtual Reality},

author = {Bruno Rodríguez-García and Henar Guillen-Sanz and David Checa and Andrés Bustillo},

url = {https://link.springer.com/article/10.1007/s11042-024-18700-3},

doi = {https://doi.org/10.1007/s11042-024-18700-3},

issn = {1573-7721},

year  = {2024},

date = {2024-04-02},

urldate = {2024-04-02},

journal = {Multimedia Tools and Applications},

abstract = {Immersive Virtual Reality (iVR) devices are increasingly affordable and accessible to consumers. The widespread adoption of this technology for professional training is now finding its way into various other fields. One field that is gaining significant popularity is Cultural Heritage (CH), where iVR enables the reconstruction and exploration of lost heritage. However, an up-to-date systematic review of iVR within this field will be of great benefit. Hence, the present review of 94 papers published between 2013 and 2022 that follows PRISMA methodology on virtual reconstruction of CH for iVR. The aim is to identify the key factors behind the development of these applications and their standards. To do so, a statistical analysis on the following topics was performed: (1) nationality, publication date, and article type; (2) heritage type and its current state of preservation; (3) the area of final application and the features of the reconstructions; (4) the characteristics of the iVR experience; and (5) the assessment of the iVR applications. Finally, a roadmap of best practices is outlined for the virtual reconstruction of CH using iVR and some of the most promising future research lines are outlined.},

keywords = {3D model, Cultural Heritage, head mounted display, Virtual Reality, Virtual Reconstruction},

pubstate = {published},

tppubtype = {article}

}

@article{Kuncheva2024,

title = {Semi-supervised classification with pairwise constraints: A case study on animal identification from video},

author = {Ludmila I. Kuncheva and José Luis Garrido-Labrador and Ismael Ramos-Pérez and Samuel L. Hennessey and Juan J. Rodríguez},

doi = {10.1016/j.inffus.2023.102188},

issn = {1566-2535},

year  = {2024},

date = {2024-04-00},

urldate = {2024-04-00},

journal = {Information Fusion},

volume = {104},

publisher = {Elsevier BV},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Ramos-Pérez2024,

title = {An Extensive Performance Comparison between Feature Reduction and Feature Selection Preprocessing Algorithms on Imbalanced Wide Data},

author = {Ismael Ramos-Pérez and José Antonio Barbero-Aparicio and Antonio Canepa-Oneto and Álvar Arnaiz-González and Jesús Maudes-Raedo},

doi = {10.3390/info15040223},

issn = {2078-2489},

year  = {2024},

date = {2024-04-00},

urldate = {2024-04-00},

journal = {Information},

volume = {15},

number = {4},

publisher = {MDPI AG},

abstract = {<jats:p>The most common preprocessing techniques used to deal with datasets having high dimensionality and a low number of instances—or wide data—are feature reduction (FR), feature selection (FS), and resampling. This study explores the use of FR and resampling techniques, expanding the limited comparisons between FR and filter FS methods in the existing literature, especially in the context of wide data. We compare the optimal outcomes from a previous comprehensive study of FS against new experiments conducted using FR methods. Two specific challenges associated with the use of FR are outlined in detail: finding FR methods that are compatible with wide data and the need for a reduction estimator of nonlinear approaches to process out-of-sample data. The experimental study compares 17 techniques, including supervised, unsupervised, linear, and nonlinear approaches, using 7 resampling strategies and 5 classifiers. The results demonstrate which configurations are optimal, according to their performance and computation time. Moreover, the best configuration—namely, k Nearest Neighbor (KNN) + the Maximal Margin Criterion (MMC) feature reducer with no resampling—is shown to outperform state-of-the-art algorithms.</jats:p>},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Maestro-Prieto2024,

title = {Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults},

author = {Jose Alberto Maestro-Prieto and José Miguel Ramírez-Sanz and Andrés Bustillo,and Juan José Rodriguez-Díez},

url = {https://doi.org/10.1007/s10489-024-05373-6},

doi = {10.1007/s10489-024-05373-6},

issn = {1573-7497},

year  = {2024},

date = {2024-03-28},

urldate = {2024-03-28},

journal = {Applied Intelligence},

abstract = {Both wear-induced bearing failure and misalignment of the powertrain between the rotor and the electrical generator are common failure modes in wind-turbine motors. In this study, Semi-Supervised Learning (SSL) is applied to a fault detection and diagnosis solution. Firstly, a dataset is generated containing both normal operating patterns and seven different failure classes of the two aforementioned failure modes that vary in intensity. Several datasets are then generated, maintaining different numbers of labeled instances and unlabeling the others, in order to evaluate the number of labeled instances needed for the desired accuracy level. Subsequently, different types of SSL algorithms and combinations of algorithms are trained and then evaluated with the test data. The results showed that an SSL approach could improve the accuracy of trained classifiers when a small number of labeled instances were used together with many unlabeled instances to train a Co-Training algorithm or combinations of such algorithms. When a few labeled instances (fewer than 10% or 327 instances, in this case) were used together with unlabeled instances, the SSL algorithms outperformed the result obtained with the Supervised Learning (SL) techniques used as a benchmark. When the number of labeled instances was sufficient, the SL algorithm (using only labeled instances) performed better than the SSL algorithms (accuracy levels of 87.04% vs. 86.45%, when labeling 10% of instances). A competitive accuracy of 97.73% was achieved with the SL algorithm processing a subset of 40% of the labeled instances.},

keywords = {Bearing failures, Fault detection and diagnosis, PID2020-119894GB-I00, Powertrain failures, Semi-supervised learning, SSL, Wind turbine},

pubstate = {published},

tppubtype = {article}

}

@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.},

keywords = {Biofeedback, Biosensors, Head-mounted displays, Heart rate, Physiology, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2024e,

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},

doi = {10.1007/s10055-023-00926-5},

issn = {1434-9957},

year  = {2024},

date = {2024-03-00},

urldate = {2024-03-00},

journal = {Virtual Reality},

volume = {28},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>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.</jats:p>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{Maestro-Prieto2024b,

title = {Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults},

author = {Jose Alberto Maestro-Prieto and José Miguel Ramírez-Sanz and Andrés Bustillo and Juan José Rodriguez-Díez},

doi = {10.1007/s10489-024-05373-6},

issn = {1573-7497},

year  = {2024},

date = {2024-03-00},

urldate = {2024-03-00},

journal = {Appl Intell},

volume = {54},

number = {6},

pages = {4525--4544},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:sec>

                <jats:title>Abstract</jats:title>

                <jats:p>Both wear-induced bearing failure and misalignment of the powertrain between the rotor and the electrical generator are common failure modes in wind-turbine motors. In this study, Semi-Supervised Learning (SSL) is applied to a fault detection and diagnosis solution. Firstly, a dataset is generated containing both normal operating patterns and seven different failure classes of the two aforementioned failure modes that vary in intensity. Several datasets are then generated, maintaining different numbers of labeled instances and unlabeling the others, in order to evaluate the number of labeled instances needed for the desired accuracy level. Subsequently, different types of SSL algorithms and combinations of algorithms are trained and then evaluated with the test data. The results showed that an SSL approach could improve the accuracy of trained classifiers when a small number of labeled instances were used together with many unlabeled instances to train a Co-Training algorithm or combinations of such algorithms. When a few labeled instances (fewer than 10% or 327 instances, in this case) were used together with unlabeled instances, the SSL algorithms outperformed the result obtained with the Supervised Learning (SL) techniques used as a benchmark. When the number of labeled instances was sufficient, the SL algorithm (using only labeled instances) performed better than the SSL algorithms (accuracy levels of 87.04% vs. 86.45%, when labeling 10% of instances). A competitive accuracy of 97.73% was achieved with the SL algorithm processing a subset of 40% of the labeled instances.</jats:p>

              </jats:sec><jats:sec>

                <jats:title>Graphical abstract</jats:title>

                <jats:p>Steps and processes for approaching semi-supervised FDD of wind-turbine gearbox misalignment and imbalance faults</jats:p>

              </jats:sec>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@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 = {Bibliographic review, Ensemble learning, Experimental protocol, Information fusion, Label scarsity, PID2020-119894GB-I00, Research trends, Semi-supervised ensemble classification, Semi-supervised learning, SSL},

pubstate = {published},

tppubtype = {article}

}

@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 = {head mounted display, Learning, novelty effect, serious games, tutorial, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@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 = {game design, game engagement, game evaluation, serious games, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{MARTICORENASANCHEZ2024101727,

title = {UBUMonitor: Desktop application for visual e-learning student clustering with Moodle},

author = {Raúl Marticorena-Sánchez and Carlos López-Nozal and Ana Serrano-Mamolar and Alicia Olivares-Gil},

url = {https://www.sciencedirect.com/science/article/pii/S2352711024000980},

doi = {https://doi.org/10.1016/j.softx.2024.101727},

issn = {2352-7110},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {SoftwareX},

volume = {26},

pages = {101727},

abstract = {UBUMonitor is an open-source Java desktop tool designed to analyse student interactions and grades within courses in the Moodle learning management system. Its primary goal is to assist educational researchers in making informed decisions. The application offers flexible configuration of educational datasets, enabling educational researchers to customize features of their analyses based on filters such as time, participants, e-resources, e-forums and teaching e-activities. With a modular architecture developed iteratively in Github with 35 releases, UBUMonitor has demonstrated its usability in teaching through several published use cases. This paper focuses specifically on the student clustering module, which allows customization of course activity access and grading, supports multiple clustering algorithms, and incorporates techniques for validating both the optimal number of clusters and the quality of cluster result.},

keywords = {e-learning analytic, Educational data clustering process, Educational software, Learning management systems, Unsupervised machine learning},

pubstate = {published},

tppubtype = {article}

}

@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 = {bioinformatics, Machine learning, PID2020-119894GB-I00, Protein fitness prediction, Semi-supervised learning, Small datasets, SSL, Transfer learning},

pubstate = {published},

tppubtype = {article}

}

@article{Ramírez-Sanz2023e,

title = {Semi-supervised learning for industrial fault detection and diagnosis: A systemic review},

author = {José Miguel Ramírez-Sanz and Jose-Alberto Maestro-Prieto and Álvar Arnaiz-González and Andrés Bustillo},

doi = {10.1016/j.isatra.2023.09.027},

issn = {0019-0578},

year  = {2023},

date = {2023-12-00},

urldate = {2023-12-00},

journal = {ISA Transactions},

volume = {143},

pages = {255--270},

publisher = {Elsevier BV},

keywords = {p_humanaid, PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Ramírez-Sanz2023f,

title = {Semi-supervised learning for industrial fault detection and diagnosis: A systemic review},

author = {José Miguel Ramírez-Sanz and Jose-Alberto Maestro-Prieto and Álvar Arnaiz-González and Andrés Bustillo},

doi = {10.1016/j.isatra.2023.09.027},

issn = {0019-0578},

year  = {2023},

date = {2023-12-00},

journal = {ISA Transactions},

volume = {143},

pages = {255--270},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2023e,

title = {Design and development of a gamified tutorial for iVR serious games},

author = {Ines Miguel-Alonso and Henar Guillen-Sanz and Bruno Rodriguez-Garcia and Andres Bustillo},

doi = {10.34190/ecgbl.17.1.1563},

issn = {2049-100X},

year  = {2023},

date = {2023-09-29},

urldate = {2023-09-29},

journal = {ECGBL},

volume = {17},

number = {1},

pages = {411--417},

publisher = {Academic Conferences International Ltd},

abstract = {<jats:p>Serious games, including immersive Virtual Reality (iVR) experiences, can be challenging for players due to their unfamiliar control systems and mechanics. This study focuses on designing a gamified tutorial for iVR serious games that not only teaches iVR interactions but also enhances user enjoyment and engagement. The tutorial consists of progressively challenging mini-games that adapt to the user's performance. Tips and recommendations are provided through a robot avatar if users struggle or make mistakes. An optional narrative is included to enhance user engagement, but it is not mandatory for the iVR experience. Gamification elements, such as point collection and progress updates, are incorporated into the tutorial. It can be played independently or as an introduction to iVR serious games. The goal is to use gamification principles to maintain user engagement and flow while enhancing the learning experience in the virtual world.</jats:p>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{Mena-Alonso2023,

title = {A cost-effective stereo camera-based system for measuring crack propagation in fibre-reinforced concrete},

author = {Álvaro Mena-Alonso and Pedro Latorre-Carmona and Dorys C. González and José F. Díez-Pastor and Juan J. Rodríguez and Jesús Mínguez and Miguel A. Vicente},

doi = {10.1007/s43452-023-00723-6},

issn = {2083-3318},

year  = {2023},

date = {2023-08-00},

urldate = {2023-08-00},

journal = {Archiv.Civ.Mech.Eng},

volume = {23},

number = {3},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>This paper shows a new low-cost technology for the measurement of crack propagation in quasi-fragile materials based on a stereo pair of cameras and LED light spots. The two cameras record the displacement experienced by a series of LED white lights. For each frame, the <jats:italic>X</jats:italic>, <jats:italic>Y</jats:italic> and <jats:italic>Z</jats:italic> 3D coordinates of all the centroids of the LED points are obtained. From this information, it is possible to determine the variation of the distance between any two of them. In this case, 2 strips of 12 LED lights each were arranged in such a way that the points of both strips coincided in pairs in height. The algorithm made it possible to monitor the increase in distance that occurred between each pair of lights at the same height. The paper shows the mathematical basis of this technological solution. A test has been carried out by installing this system in a concrete cube 150 mm side and subjected to a wedge-splitting test. The results show that it is possible to monitor the crack propagation (position of the crack front) during the test and to know the crack width too. At present, the accuracy of this technique is only limited by the camera resolution and the computer processing capability.</jats:p>},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Serrano-Mamolar2023,

title = {Towards learner performance evaluation in iVR learning environments using eye-tracking and Machine-learning},

author = {Ana Serrano-Mamolar and Ines Miguel-Alonso and David Checa and Carlos Pardo-Aguilar},

doi = {10.3916/c76-2023-01},

issn = {1988-3293},

year  = {2023},

date = {2023-07-01},

urldate = {2023-07-01},

journal = {Comunicar: Media Education Research Journal},

volume = {31},

number = {76},

publisher = {Oxbridgepublishinghouse},

abstract = {<jats:p>At present, the use of eye-tracking data in immersive Virtual Reality (iVR) learning environments is set to become a powerful tool for maximizing learning outcomes, due to the low-intrusiveness of eye-tracking technology and its integration in commercial iVR Head Mounted Displays. However, the most suitable technologies for data processing should first be identified before their use in learning environments can be generalized. In this research, the use of machine-learning techniques is proposed for that purpose, evaluating their capabilities to classify the quality of the learning environment and to predict user learning performance. To do so, an iVR learning experience simulating the operation of a bridge crane was developed. Through this experience, the performance of 63 students was evaluated, both under optimum learning conditions and under stressful conditions. The final dataset included 25 features, mostly temporal series, with a dataset size of up to 50M data points. The results showed that different classifiers (KNN, SVM and Random Forest) provided the highest accuracy when predicting learning performance variations, while the accuracy of user learning performance was still far from optimized, opening a new line of future research. This study has the objective of serving as a baseline for future improvements to model accuracy using complex machine-learning techniques.</jats:p>

<jats:p>Actualmente, el uso de los datos del seguimiento de la mirada en entornos de aprendizaje de Realidad Virtual inmersiva (iVR) está destinado a ser una herramienta fundamental para maximizar los resultados de aprendizaje, dada la naturaleza poco intrusiva del eye-tracking y su integración en las gafas comerciales de Realidad Virtual. Pero, antes de que se pueda generalizar el uso del eye-tracking en entornos de aprendizaje, se deben identificar las tecnologías más adecuadas para el procesamiento de datos. Esta investigación propone el uso de técnicas de aprendizaje automático para este fin, evaluando sus capacidades para clasificar la calidad del entorno de aprendizaje y predecir el rendimiento de aprendizaje del usuario. Para ello, se ha desarrollado una experiencia docente en iVR para aprender el manejo de un puente-grúa. Con esta experiencia se ha evaluado el rendimiento de 63 estudiantes, tanto en condiciones óptimas de aprendizaje como en condiciones con factores estresores. El conjunto de datos final incluye 25 características, siendo la mayoría series temporales con un tamaño de conjunto de datos superior a 50 millones de puntos. Los resultados muestran que la aplicación de diferentes clasificadores como KNN, SVM o Random Forest tienen una alta precisión a la hora de predecir alteraciones en el aprendizaje, mientras que la predicción del rendimiento del aprendizaje del usuario aún está lejos de ser óptima, lo que abre una nueva línea de investigación futura. Este estudio tiene como objetivo servir como línea de base para futuras mejoras en la precisión de los modelos mediante el uso de técnicas de aprendizaje automático más complejas.</jats:p>},

keywords = {eye_rv_dataset, p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{romero2023,

title = {Tuning 3D-printing parameters to produce vertical ultra-hydrophobic PETG parts with low ice adhesion: A food industry case study},

author = {Pablo E. Romero and Barrios Juan M and Esther Molero and Andres Bustillo},

url = {https://doi.org/10.1177/09544054231178970},

doi = {10.1177/09544054231178970},

year  = {2023},

date = {2023-06-06},

urldate = {2023-06-06},

journal = {Proc IMechE Part B: J Engineering Manufacture},

pages = {1-9},

abstract = {The food industry is a dynamic component of the European economy. A wide variety of products and small batch are demanded in a market that is accustomed to frequenting changes in food packaging formats. Cheaper and lighter 3D-printed tools are replacing expensive metallic ones, producing previously impossible product geometries and processing fish and meat products more quickly and in more reliable ways. In addition to food contact, these printed parts are often required to have hydrophobic surfaces that facilitate cleaning and have low adhesion both foodstuffs and ice. In this study, the surface wettability of PolyEthylene Terephthalate Glycol (PETG) printed parts via fused filament fabrication is assessed. Specifically, several printing parameters (layer height, extrusion temperature, printing speed, acceleration, and flow) and their influence on the hydrophobicity of 3D printed parts with vertical orientation are analyzed. The experimental results indicated that the parameter with the strongest influence on the wettability of the XZ parts was flow: low-flow values generated ultra-hydrophobic surfaces, with contact angles higher than 120°. Acceleration had no influence at low flow values; however, for high flow values, low acceleration rates yielded higher contact angles. In addition, it was experimentally proven that the 3D-printed PETG parts with high-contact angle surfaces showed lower adhesion to ice than those with low contact-angle surfaces. The technology was applied to a case study of a 3D-printed hopper for the ice duct of an ice-cube machine.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kuncheva2023,

title = {An experiment on animal re-identification from video},

author = {Ludmila I. Kuncheva and José Luis Garrido-Labrador and Ismael Ramos-Pérez and Samuel L. Hennessey and Juan J. Rodríguez},

doi = {10.1016/j.ecoinf.2023.101994},

issn = {1574-9541},

year  = {2023},

date = {2023-05-00},

urldate = {2023-05-00},

journal = {Ecological Informatics},

volume = {74},

publisher = {Elsevier BV},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{barbero-aparicio2023,

title = {Deep learning and support vector machines for transcription start site identification},

author = {José A. Barbero-Aparicio and Alicia Olivares-Gil and José F. Díez-Pastor and César García-Osorio},

editor = {Carlos Fernandez-Lozano},

url = {https://doi.org/10.7717/peerj-cs.1340},

doi = {10.7717/peerj-cs.1340},

issn = {2376-5992},

year  = {2023},

date = {2023-04-17},

urldate = {2023-04-17},

journal = {PeerJ Computer Science},

volume = {9},

issue = {e1340},

abstract = {Recognizing transcription start sites is key to gene identification. Several approaches have been employed in related problems such as detecting translation initiation sites or promoters, many of the most recent ones based on machine learning. Deep learning methods have been proven to be exceptionally effective for this task, but their use in transcription start site identification has not yet been explored in depth. Also, the very few existing works do not compare their methods to support vector machines (SVMs), the most established technique in this area of study, nor provide the curated dataset used in the study. The reduced amount of published papers in this specific problem could be explained by this lack of datasets. Given that both support vector machines and deep neural networks have been applied in related problems with remarkable results, we compared their performance in transcription start site predictions, concluding that SVMs are computationally much slower, and deep learning methods, specially long short-term memory neural networks (LSTMs), are best suited to work with sequences than SVMs. For such a purpose, we used the reference human genome GRCh38. Additionally, we studied two different aspects related to data processing: the proper way to generate training examples and the imbalanced nature of the data. Furthermore, the generalization performance of the models studied was also tested using the mouse genome, where the LSTM neural network stood out from the rest of the algorithms. To sum up, this article provides an analysis of the best architecture choices in transcription start site identification, as well as a method to generate transcription start site datasets including negative instances on any species available in Ensembl. We found that deep learning methods are better suited than SVMs to solve this problem, being more efficient and better adapted to long sequences and large amounts of data. We also create a transcription start site (TSS) dataset large enough to be used in deep learning experiments},

keywords = {bioinformatics, Convolutional neural network, Deep learning, Long short-term memory, Machine learning, PID2020-119894GB-I00, Support vector machines, transcription start site},

pubstate = {published},

tppubtype = {article}

}

@article{ramirez-sanz2023,

title = {A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept},

author = {José Miguel Ramírez-Sanz and José Luis Garrido-Labrador and Alicia Olivares-Gil and Álvaro García-Bustillo and Álvar Arnaiz-González and José-Francisco Díez-Pastor and Maha Jahouh and Josefa González-Santos and Jerónimo J. González-Bernal and Marta Allende-Río and Florita Valiñas-Sieiro and Jose M. Trejo-Gabriel-Galan and Esther Cubo},

editor = {Maria-Esther Vidal and José Alberto Benítez Andrades and Alejandro Rodríguez-González},

url = {https://www.mdpi.com/2227-9032/11/4/507},

doi = {10.3390/healthcare11040507},

issn = {2227-9032},

year  = {2023},

date = {2023-02-09},

urldate = {2023-02-09},

journal = {Healthcare},

volume = {11},

number = {507},

issue = {4},

abstract = {first_page 

settings 

Order Article Reprints 

Open AccessArticle 

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept 

by José Miguel Ramírez-Sanz 

1 [ORCID] , José Luis Garrido-Labrador 

1 [ORCID] , Alicia Olivares-Gil 

1 [ORCID] , Álvaro García-Bustillo 

2 [ORCID] , Álvar Arnaiz-González 

1,* [ORCID] , José-Francisco Díez-Pastor 

1, Maha Jahouh 

3, Josefa González-Santos 

3, Jerónimo J. González-Bernal 

3 [ORCID] , Marta Allende-Río 

4, Florita Valiñas-Sieiro 

4, Jose M. Trejo-Gabriel-Galan 

4 and Esther Cubo 

4 

1 

Escuela Politécnica Superior, Departamento de Ingeniería Informática, Universidad de Burgos, Avda. Cantabria s/n, 09006 Burgos, Spain 

2 

Fundación Burgos por la Investigación de la Salud, 09006 Burgos, Spain 

3 

Departamento de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, Paseo Comendadores s/n, 09001 Burgos, Spain 

4 

Servicio de Neurología, Hospital Universitario de Burgos, 09006 Burgos, Spain 

* 

Author to whom correspondence should be addressed. 

Healthcare 2023, 11(4), 507; https://doi.org/10.3390/healthcare11040507 (registering DOI) 

Received: 19 December 2022 / Revised: 20 January 2023 / Accepted: 7 February 2023 / Published: 9 February 2023 

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics) 

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Versions Notes 

The consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson’s disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs.},

keywords = {artificial intelligence in healthcare, Big data, Parkinson's disease, telemedicine, telerehabilitation},

pubstate = {published},

tppubtype = {article}

}

@article{miguel-alonso2023,

title = {Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments},

author = {Inés Miguel-Alonso and Bruno Rodriguez-Garcia and David Checa and Andrés Bustillo},

editor = {MDPI},

url = {https://www.mdpi.com/2076-3417/13/1/593 

https://www.mdpi.com/journal/applsci/special_issues/HK43Y20XK5},

doi = {10.3390/app13010593},

issn = {2076-3417},

year  = {2023},

date = {2023-01-02},

urldate = {2023-01-02},

journal = {Applied Sciences},

volume = {13},

number = {593},

issue = {1},

abstract = {Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels had no influence on flow and immersion and little influence on skill, presence, and engagement, greatly reducing the benefits of the tutorial, despite which it remained useful.},

keywords = {cybersickness, education, novelty effect, tutorial, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2023b,

title = {Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments},

author = {Ines Miguel-Alonso and Bruno Rodriguez-Garcia and David Checa and Andres Bustillo},

doi = {10.3390/app13010593},

issn = {2076-3417},

year  = {2023},

date = {2023-01-00},

journal = {Applied Sciences},

volume = {13},

number = {1},

publisher = {MDPI AG},

abstract = {Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels had no influence on flow and immersion and little influence on skill, presence, and engagement, greatly reducing the benefits of the tutorial, despite which it remained useful.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2023d,

title = {Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments},

author = {Ines Miguel-Alonso and Bruno Rodriguez-Garcia and David Checa and Andres Bustillo},

doi = {10.3390/app13010593},

issn = {2076-3417},

year  = {2023},

date = {2023-01-00},

urldate = {2023-01-00},

journal = {Applied Sciences},

volume = {13},

number = {1},

publisher = {MDPI AG},

abstract = {<jats:p>Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels had no influence on flow and immersion and little influence on skill, presence, and engagement, greatly reducing the benefits of the tutorial, despite which it remained useful.</jats:p>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{Arnau-González2023b,

title = {Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems},

author = {Pablo Arnau-González and Ana Serrano-Mamolar and Stamos Katsigiannis and Turke Althobaiti and Miguel Arevalillo-Herráez},

doi = {10.1109/access.2023.3290478},

issn = {2169-3536},

year  = {2023},

date = {2023-00-00},

urldate = {2023-00-00},

journal = {IEEE Access},

volume = {11},

pages = {67030--67039},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{Setó-Rey2023,

title = {Vulnerability of Package Dependency Networks},

author = {Daniel Setó-Rey and José Ignacio Santos-Martín and Carlos López-Nozal},

doi = {10.1109/tnse.2023.3260880},

issn = {2327-4697},

year  = {2023},

date = {2023-00-00},

urldate = {2023-00-00},

journal = {IEEE Trans. Netw. Sci. Eng.},

pages = {1--13},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Barbero-Aparicio2022,

title = {Nonlinear physics opens a new paradigm for accurate transcription start site prediction},

author = {José Antonio Barbero-Aparicio and Santiago Cuesta-Lopez and César Ignacio García-Osorio and Javier Pérez-Rodríguez and Nicolás García-Pedrajas},

editor = {José Manuel Benítez},

url = {https://doi.org/10.1186/s12859-022-05129-4},

doi = {10.1186/s12859-022-05129-4},

issn = {1471-2105},

year  = {2022},

date = {2022-12-30},

urldate = {2022-12-30},

journal = {BMC Bioinformatics},

volume = {23},

number = {565},

abstract = {There is evidence that DNA breathing (spontaneous opening of the DNA strands) plays a relevant role in the interactions of DNA with other molecules, and in particular in the transcription process. Therefore, having physical models that can predict these openings is of interest. However, this source of information has not been used before either in transcription start sites (TSSs) or promoter prediction. In this article, one such model is used as an additional information source that, when used by a machine learning (ML) model, improves the results of current methods for the prediction of TSSs. In addition, we provide evidence on the validity of the physical model, as it is able by itself to predict TSSs with high accuracy. This opens an exciting avenue of research at the intersection of statistical mechanics and ML, where ML models in bioinformatics can be improved using physical models of DNA as feature extractors.},

keywords = {DNA breathing, DNA modelling, Machine learning, String kernels, SVM, TSS prediction},

pubstate = {published},

tppubtype = {article}

}

@article{kim2022,

title = {A New Measure for Serious Games Evaluation: Gaming Educational Balanced (GEB) Model},

author = {Kim Martinez and María Isabel Menéndez-Menéndez and Andres Bustillo},

editor = {Maya Satratzemi and Stelios Xinogalos},

doi = {10.3390/app122211757},

issn = {2076-3417},

year  = {2022},

date = {2022-11-19},

urldate = {2022-11-19},

journal = {Applied Sciences},

volume = {12},

number = {11757},

issue = {22},

abstract = {Serious games have to meet certain characteristics relating to gameplay and educational content to be effective as educational tools. There are some models that evaluate these aspects, but they usually lack a good balance between both ludic and learning requirements, and provide no guide for the design of new games. This study develops the Gaming Educational Balanced (GEB) Model which addresses these two limitations. GEB is based on the Mechanics, Dynamics and Aesthetics framework and the Four Pillars of Educational Games theory. This model defines a metric to evaluate serious games, which can also be followed to guide their subsequent development. This rubric is tested with three indie serious games developed using different genres to raise awareness of mental illnesses. This evaluation revealed two main issues: the three games returned good results for gameplay, but the application of educational content was deficient, due in all likelihood to the lack of expert educators participating in their development. A statistical and machine learning validation of the results is also performed to ensure that the GEB metric features are clearly explained and the players are able to evaluate them correctly. These results underline the usefulness of the new metric tool for identifying game design strengths and weaknesses. Future works will apply this metric to more serious games to further test its effectiveness and to guide the design of new serious games.},

keywords = {game design, game evaluation, metrics, serious games},

pubstate = {published},

tppubtype = {article}

}

@article{Pimenov2022,

title = {Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review},

author = {Danil Yurievich Pimenov and Andrés Bustillo and Szymon Wojciechowski and Vishal Santosh Sharma and Munish Kumar Gupta and Mustafa Kuntğlu},

url = {https://link.springer.com/article/10.1007/s10845-022-01923-2#citeas},

doi = {10.1007/s10845-022-01923-2},

issn = {0956-5515},

year  = {2022},

date = {2022-03-12},

urldate = {2022-03-12},

journal = {Journal of Intelligent Manufacturing},

volume = {2022},

abstract = {The wear of cutting tools, cutting force determination, surface roughness variations and other machining responses are of keen interest to latest researchers. The variations of these machining responses results in change in dimensional accuracy and productivity upto great extent. In addition, an excessive increase in wear leads to catastrophic consequences, exceeding the tool breakage. Therefore, this article discusses the online trend of modern approaches in tool condition monitoring while different machining operations. For this purpose, the effective use of new sensors and artificial intelligence (AI) is considered and followed during this holistic review work. The sensor systems used for monitoring tool wear are dynamometers, accelerometers, acoustic emission sensors, current and power sensors, image sensors, other sensors. These systems allow to solve the problem of automation and modeling of technological parameters of the main types of cutting, such as turning, milling, drilling and grinding. The modern artificial intelligence methods are considered, such as: Neural networks, Image recognition, Fuzzy logic, Adaptive neuro-fuzzy inference systems, Bayesian Networks, Support vector machine, Ensembles, Decision and regression trees, k-nearest neighbors, Artificial Neural Network, Markov model, Singular Spectrum Analysis, Genetic algorithms. Discussions also includes the main advantages, disadvantages and prospects of using various AI methods for tool wear monitoring. Moreover, the problems and future directions of the main processing methods using AI models are also highlighted.},

keywords = {Artificial intelligence, Machining, PID2020-119894GB-I00, Sensor, tool condition monitoring, Tool life, Wear},

pubstate = {published},

tppubtype = {article}

}

@article{Ramos-Pérez2022,

title = {When is resampling beneficial for feature selection with imbalanced wide data?},

author = {Ismael Ramos-Pérez and Álvar Arnaiz-González and Juan José Rodríguez and César García-Osorio},

url = {https://www.sciencedirect.com/science/article/pii/S0957417421013622},

doi = {https://doi.org/10.1016/j.eswa.2021.116015},

issn = {0957-4174},

year  = {2022},

date = {2022-02-01},

urldate = {2022-02-01},

journal = {Expert Systems with Applications},

volume = {188},

pages = {116015},

abstract = {This paper studies the effects that combinations of balancing and feature selection techniques have on wide data (many more attributes than instances) when different classifiers are used. For this, an extensive study is done using 14 datasets, 3 balancing strategies, and 7 feature selection algorithms. The evaluation is carried out using 5 classification algorithms, analyzing the results for different percentages of selected features, and establishing the statistical significance using Bayesian tests. 

Some general conclusions of the study are that it is better to use RUS before the feature selection, while ROS and SMOTE offer better results when applied afterwards. Additionally, specific results are also obtained depending on the classifier used, for example, for Gaussian SVM the best performance is obtained when the feature selection is done with SVM-RFE before balancing the data with RUS.},

keywords = {Feature selection, High dimensional data, Machine learning, PID2020-119894GB-I00, SELECTED, Unbalanced, Very low sample size, Wide data},

pubstate = {published},

tppubtype = {article}

}