Guevara Maldonado, César Byron
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Guevara Maldonado, César Byron
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Quito
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cesarguevara@uti.edu.ec
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56452706300

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Accessibility assessment in mobile applications for android

2020 , Acosta-Vargas, P. , Salvador-Ullauri, L. , Jadán Guerrero, Janio , Guevara Maldonado, César Byron , Sanchez-Gordon, S. , Calle-Jimenez, T. , Lara Álvarez, Patricio , Medina, A. , Nunes, I.L.

At present, the lack of adequate methods to test whether a mobile application is accessible has become a major challenge for accessibility experts. This study was applied to ten mobile applications, the most popular according to PCMAG. We propose to use the Web Content Accessibility Guidelines 2.1 through manual review and automatic review with the Google Play Store Accessibility Scanner validator for the Android. The evaluation results of the mobile applications indicate that the applications are not accessible because they do not comply with the minimum required level proposed by WCAG 2.1. The research proposes suggestions to improve and raise awareness among the designers of mobile applications, in such a way that more inclusive mobile applications accessible to all types of users are built. © Springer Nature Switzerland AG 2020.

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Development of Behavior Profile of Users with Visual Impairment

2020 , Guevara Maldonado, César Byron , Arias Flores, Hugo Patricio , Varela Aldas, José , Castillo Salazar, David Ricardo , Borja M. , Fierro-Saltos W. , Rivera R. , Hidalgo-Guijarro J. , Yandún-Velasteguí M.

The interaction of the user with visual impairment with assistive technologies, and in particular with screen readers, generates a group of actions and events during their navigation. These interactions are defined as behavioral patterns, which have a sequence that occurs at specific time slot. Understanding user behavior by analyzing their interaction with applications, in addition, details the characteristics, relationships, structures and functions of the sequence of actions in a specific application domain. The objective of this document is to find activity patterns from a set of commands used by the user, combining data mining and a Bayesian model. This model calculates the probability of the functions used with the screen reader and generates a behavior profile to improve the user experience. For this study, the screen reader JAWS version 2018, the Open Journal Systems platform version 3.0.1 and a computer with Windows 10 operating system were used. During the first phase, command history used by the user by interacting with the Open Journal Systems platform were collected. The result is that the accessibility of users with visual impairment to interact with the computer and its applications has been improved by applying this model. © Springer Nature Switzerland AG 2020.

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BackMov: Individualized Motion Capture-Based Test to Assess Low Back Pain Mobility Recovery after Treatment

2024 , Villalba-Meneses F. , Guevara Maldonado, César Byron , Velásquez-López P.A. , Arias-Serrano I. , Guerrero-Ligña S.A. , Valencia-Cevallos C.M. , Almeida-Galárraga D. , Cadena-Morejón C. , Marín J., Marín J.J.

Low back pain (LBP) is a common issue that negatively affects a person’s quality of life and imposes substantial healthcare expenses. In this study, we introduce the (Back-pain Movement) BackMov test, using inertial motion capture (MoCap) to assess lumbar movement changes in LBP patients. The test includes flexion–extension, rotation, and lateralization movements focused on the lumbar spine. To validate its reproducibility, we conducted a test-retest involving 37 healthy volunteers, yielding results to build a minimal detectable change (MDC) graph map that would allow us to see if changes in certain variables of LBP patients are significant in relation to their recovery. Subsequently, we evaluated its applicability by having 30 LBP patients perform the movement’s test before and after treatment (15 received deep oscillation therapy; 15 underwent conventional therapy) and compared the outcomes with a specialist’s evaluations. The test-retest results demonstrated high reproducibility, especially in variables such as range of motion, flexion and extension ranges, as well as velocities of lumbar movements, which stand as the more important variables that are correlated with LBP disability, thus changes in them may be important for patient recovery. Among the 30 patients, the specialist’s evaluations were confirmed using a low-back-specific Short Form (SF)-36 Physical Functioning scale, and agreement was observed, in which all patients improved their well-being after both treatments. The results from the specialist analysis coincided with changes exceeding MDC values in the expected variables. In conclusion, the BackMov test offers sensitive variables for tracking mobility recovery from LBP, enabling objective assessments of improvement. This test has the potential to enhance decision-making and personalized patient monitoring in LBP management. © 2024 by the authors.

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Framework based on gestalt principles to design mobile interfaces for a better user experience

2020 , Ripalda D. , Guevara Maldonado, César Byron , Garrido A.

This paper presents the results of the user experience test comparing a real functional application and a high fidelity prototype that used a Framework to design graphic user interfaces on mobile devices. This Framework links Nielsen’s heuristics with the principles of perception of Gestalt, offering to developers and usability experts, references to generate and evaluate mockups and prototypes. The constructive and evaluative model of the Framework allows to recognize usability criteria in visual components of the interfaces, during the initial phases of a project that uses agile software development methodologies, reducing the “trial - error” regressions. The experiment allowed obtaining data about satisfaction measures and specific user attitudes regarding the interfaces developed. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

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Biomechanical Study of the Eye with Keratoconus-Type Corneal Ectasia Using a 3D Geometric Model

2023 , Sánchez-Real E. , Otuna-Hernández D., , Fajardo-Cabrera A. , Davies-Alcívar R. , Madrid-Pérez M. , Cadena-Morejón C. , Almeida-Galárraga D. , Guevara Maldonado, César Byron , Tirado-Espín A. , Villalba-Meneses F.

Keratoconus is an eye disease that distorts the shape of the cornea. This study aimed to analyze the effect of an increase in intraocular pressure applied to eyes with different severity of keratoconus disease using patient-specific models. Finite element models of the normal eye, eye with keratoconus, and eye with keratoglobus were constructed. The loading conditions considered the intraocular pressure increment as well as their physiological intraocular pressure. The analysis was performed with distinct materials for normal and keratoconic eyes. The finite element analysis revealed differences in the three models in terms of their deformation and maximum principal stress, and differences were observed in corneal curvature and thickness. These findings could enhance research in the biomechanical area, leading to more successful treatment options and a more individualized approach in the field of practical ophthalmology. Further investigation with larger sample sizes and more precise data on eye material would allow us to evaluate whether these disparities could inform the diagnosis of keratoconus. © 2023 by the authors.

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Twitter Mining for Multiclass Classification Events of Traffic and Pollution

2020 , Chamorro V. , Rivera R. , Varela Aldas, José , Castillo Salazar, David Ricardo , Borja Galeas, Carlos , Guevara Maldonado, César Byron , Arias Flores, Hugo Patricio , Fierro-Saltos W. , Hidalgo-Guijarro J. , Yandún-Velasteguí M.

During the last decade social media have generated tons of data, that is the primal information resource for multiple applications. Analyzing this information let us to discover almost immediately unusual situations, such as traffic jumps, traffic accidents, state of the roads, etc. This research proposes an approach for classifying pollution and traffic tweets automatically. Taking advantage of the information in tweets, it evaluates several machine learning supervised algorithms for text classification, where it determines that the support vector machine (SVM) algorithm achieves the highest accuracy value of 85,8% classifying events of traffic and not traffic. Furthermore, to determine the events that correspond to traffic or pollution we perform a multiclass classification. Where we obtain an accuracy of 78.9%. © Springer Nature Switzerland AG 2020.

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Diagnosis and Degree of Evolution in a Keratoconus-Type Corneal Ectasia from Image Processing

2023 , Otuna-Hernández D. , Espinoza-Castro L. , Yánez-Contreras P. , Villalba-Meneses F. , Cadena-Morejón C. , Guevara Maldonado, César Byron , Cruz-Varela J. , Tirado-Espín A. , Almeida-Galárraga D.

Keratoconus is a degenerative ocular pathology characterized by the thinning of the cornea, thus affecting many people around the world since this corneal ectasia causes a deformation of the corneal curvature that leads to astigmatism and, in more severe cases, to blindness. Treating physicians use non-invasive instruments, such is the case of Pentacam®, which takes images of the cornea, both the topography and the profile of the cornea, which allows them to diagnose, evaluate and treat this disease; this is known as morphological characterization of the cornea. On the other hand, Berlin/Ambrosio analysis helps in the identification and subsequent diagnosis since this analysis uses a mathematical model of linear progression, which identifies the different curves with the severity of the disease. Therefore, the aim of this study is to use the images provided by Pentacam®, Berlin/Ambrosio analysis, and vision parameters in a convolutional neural network to evaluate if this disparity could be used to help with the diagnosis of keratoconus and, consequently, generate a more precise and optimal method in the diagnosis of keratoconus. As a result, the processing and comparison of the images and the parameters allowed a 10% increase in the results of specificity and sensitivity of the mean and severe stages when combining tools (corneal profile and vision parameters) in the CNN reaching ranges of 90 to 95%. Furthermore, it is important to highlight that in the early-stage study, its improvement was around 20% in specificity, sensitivity, and accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Emotion classification using EEG headset signals and Random Forests [Clasificación de emociones utilizando señales de auriculares EEG y Random Forests]

2023 , Vasquez R. , Carrion-Jumbo J. , Riofrio-Luzcando D. , Guevara Maldonado, César Byron

Emotions are one of the important components of the human being, thus they are a valuable part of daily activities such as interaction with people, decision making and learning. For this reason, it is important to detect, recognize and understand emotions using computational systems to improve communication between people and machines, which would facilitate the ability of computers to understand the communication between humans. This study proposes the creation of a model that allows the classification of people's emotions based on their EEG signals, for which the brain-computer interface EMOTIV EPOC was used. This allowed the collection of electroencephalographic information from 50 people, all of whom were shown audiovisual resources that helped to provoke the desired mood. The information obtained was stored in a database for the generation of the model and the corresponding classification analysis. Random Forest model was created for emotion prediction (happiness, sadness and relaxation), based on the signals of any person. The results obtained were 97.21% accurate for happiness, 76% for relaxation and 76% for sadness. Finally, the model was used to generate a real-time emotion prediction algorithm; it captures the person's EEG signals, executes the generated algorithm and displays the result on the screen with the help of images representative of each emotion. © 2023 ITMA.

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Artificial Intelligence in Higher Education: A Predictive Model for Academic Performance

2023 , Pacheco-Mendoza S. , Guevara Maldonado, César Byron , Mayorga-Albán A. , Fernández-Escobar J.

This research work evaluates the use of artificial intelligence and its impact on student’s academic performance at the University of Guayaquil (UG). The objective was to design and implement a predictive model to predict academic performance to anticipate student performance. This research presents a quantitative, non-experimental, projective, and predictive approach. A questionnaire was developed with the factors involved in academic performance, and the criterion of expert judgment was used to validate the questionnaire. The questionnaire and the Google Forms platform were used for data collection. In total, 1100 copies of the questionnaire were distributed, and 1012 responses were received, representing a response rate of 92%. The prediction model was designed in Gretl software, and the model fit was performed considering the mean square error (0.26), the mean absolute error (0.16), and a coefficient of determination of 0.9075. The results show the statistical significance of age, hours, days, and AI-based tools or applications, presenting p-values < 0.001 and positive coefficients close to zero, demonstrating a significant and direct effect on students’ academic performance. It was concluded that it is possible to implement a predictive model with theoretical support to adapt the variables based on artificial intelligence, thus generating an artificial intelligence-based mode. © 2023 by the authors.

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COVID-19 spread algorithm in the international airport network-DetArpds

2023 , Guevara Maldonado, César Byron , Coronel D. , Maldonado B.E.S. , Flores J.E.S.

Due to COVID-19, the spread of diseases through air transport has become an important issue for public health in countries globally. Moreover, mass transportation (such as air travel) was a fundamental reason why infections spread to all countries within weeks. In the last 2 years in this research area, many studies have applied machine learning methods to predict the spread of COVID-19 in different environments with optimal results. These studies have implemented algorithms, methods, techniques, and other statistical models to analyze the information in accuracy form. Accordingly, this study focuses on analyzing the spread of COVID-19 in the international airport network. Initially, we conducted a review of the technical literature on algorithms, techniques, and theorems for generating routes between two points, comprising an analysis of 80 scientific papers that were published in indexed journals between 2017 and 2021. Subsequently, we analyzed the international airport database and information on the spread of COVID-19 from 2020 to 2022 to develop an algorithm for determining airport routes and the prevention of disease spread (DetARPDS). The main objective of this computational algorithm is to generate the routes taken by people infected with COVID-19 who transited the international airport network. The DetARPDS algorithm uses graph theory to map the international airport network using geographic allocations to position each terminal (vertex), while the distance between terminals was calculated with the Euclidian distance. Additionally, the proposed algorithm employs the Dijkstra algorithm to generate route simulations from a starting point to a destination air terminal. The generated routes are then compared with chronological contagion information to determine whether they meet the temporality in the spread of the virus. Finally, the obtained results are presented achieving a high probability of 93.46% accuracy for determining the entire route of how the disease spreads. Above all, the results of the algorithm proposed improved different computational aspects, such as time processing and detection of airports with a high rate of infection concentration, in comparison with other similar studies shown in the literature review. © 2023 Guevara et al.

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