Ingeniería de Sistemas-Pregrado

Permanent URI for this collectionhttps://dspace-test.ucuenca.edu.ec/handle/123456789/18

Tesis de pregrado de la Facultad de Ingeniería, Escuela de Sistemas

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Sistema informático para el control del distributivo docente y el control de horarios basados en métodos heurísticos. Caso de estudio: Facultad de Ciencias Económicas
(Universidad de Cuenca, 2025-10-28) Viñanzaca Padilla, Víctor Alfonso; Sagbay Peña, Blanca Elizabeth; Veintimilla Reyes, Jaime Eduardo; Roldán Monsalve, Diego Fernando
The administrative staff of the Faculty of Economic and Administrative Sciences at the University of Cuenca faces the task of creating class schedules each cycle, which requires considerable resource allocation and time. This process, currently done manually using tools like Excel, involves variables such as teacher availability, classroom capacity, and course preferences, which can lead to errors and delays. The lack of an adequate system complicates the delivery of information to teachers, students, and administrative staff. To address these issues, an information system was developed to optimize the management of schedules and academic distributions, based on user experience. This system was specifically designed for the Faculty of Economics, after gathering its needs through meetings with its staff and referencing the AscTables system used by the Faculty of Engineering. The system was tested at the Faculty of Economics, which has a large population of teachers and students, and was used for scheduling planning for the periods of September 2023 and March 2024, receiving a favorable evaluation from the authorities. It is believed that this system will also be useful for other faculties requiring similar solutions.
Arquitectura de microservicios para aplicaciones de gestión empresarial: estrategias modulares para escalabilidad y flexibilidad
(Universidad de Cuenca, 2025-09-25) Peñaloza Zhañay, Michael Enmanuel; Zúñiga Prieto, Miguel Ángel
Modern business applications require software structures that allow for evolution, scalability, and maintenance without affecting the overall operation of the system. However, many current systems have limitations because they are based on monolithic architectures, making it difficult to adapt to new processes and technologies. In this context, microservices architecture is presented as an alternative for designing more modular and decoupled business applications. This thesis proposes the design of a microservices-based software architecture applied to a commercial management system. The design was developed using the 4SRS-MSLA method, which guides the transformation of functional requirements into logical components. Complementarily, SoaML notation was used to model participants, interfaces, and contracts between microservices, ensuring clarity in the specification of services and interaction flows. As part of the process, practical tools such as a block diagram and an API table were incorporated, which facilitated communication with the stakeholder and allowed for early validation of the expected services between modules. The detailed design of the most important microservices for the stakeholder was also carried out, and a functional prototype was built that integrated these microservices. Through a representative functional case, the interoperability between services was tested and the technical feasibility of the design was confirmed. The proposed architecture proves to be a feasible, modular, and scalable solution, capable of guiding the development of modern service-oriented business systems, thus fulfilling the objectives set out in this research.
Análisis de anomalías climáticas en series temporales de precipitación en el Austro ecuatoriano utilizando fuentes textuales
(Universidad de Cuenca, 2025-09-25) Zambrano Rojas, Johnny Steven; Saquicela Galarza, Víctor Hugo
The increase in climate variability and the growing frequency of extreme events represent significant challenges for society and the environment. Gaining a deep understanding of past anomalous events is essential for planning, adaptation, and mitigation of the impact of similar events in the future. Climate time series contain valuable information about these patterns, but analyzing and interpreting significant deviations requires advanced analytical tools. Although established methods exist for anomaly detection in time series, they often lack enriched contextual interpretation. The absence of this contextualization limits the practical value of anomaly detection for informed decision-making and anticipating future scenarios. This work proposes a process that integrates anomaly detection techniques in time series with the incorporation of contextual information from textual sources. Statistical approaches, machine learning, deep learning, and large language models are used to identify significant deviations in historical precipitation data. The detected anomalies are complemented with relevant information extracted from news sources through a manual and semi-automated process, and are stored in a structured relational database. Additionally, a numerical and visual similarity analysis is implemented to compare anomalies with one another, enabling the identification of recurring patterns over time and providing a more comprehensive un derstanding of extreme climate events.
Arquitectura IoT para la automatización agrícola: captura del conocimiento ancestral y gestión en entornos de conectividad limitada
(Universidad de Cuenca, 2025-09-25) Orellana Salinas, Juan Diego; Pacurucu Llivichuzhca, Carlos Samuel; Zúñiga Prieto, Miguel Ángel; Palacio Baus, Kenneth Samuel
AAgriculture is a fundamental sector in developing countries such as Ecuador, where it is still practiced in a traditional way with limited access to technologies that optimize production. IoT-based solutions have shown potential to automate tasks such as irrigation, fertilization, and environmental monitoring; however, they often rely on constant connectivity and energy supply, which limits their effectiveness in rural areas with deficient infrastructure. Moreover, these solutions usually focus solely on automation, without considering mechanisms that preserve the ancestral knowledge of farmers. This work aimed to design a distributed software architecture for IoT applications in agricultural contexts with limited connectivity, oriented toward task monitoring and the preservation of ancestral knowledge. The proposal includes: (1) detecting agricultural practices through environmental data captured by sensors, (2) recording farmers’ decisions and techniques, and (3) correlating both sources to digitalize ancestral knowledge, validate patterns through machine learning, and generate adaptive recommendations that integrate traditional wisdom with precision agriculture. The methodology applied was RUP, and the architecture was documented using Kruchten’s 4+1 views model, also applying SOLID principles. A prototype was implemented with Flutter and Isar for the mobile application, Angular for the web environment, RabbitMQ for asynchronous messaging, Spring Boot with PostgreSQL for the central server, and a local node developed in Python on Raspberry Pi. The results demonstrate autonomy under intermittent connectivity, fault tolerance, scalability, energy efficiency, and a modular, resilient, and replicable solution that promotes technological equity and sustainability in rural contexts.
Análisis del impacto del reconocimiento facial en el enganche del jugador en videojuegos: un estudio de caso
(Universidad de Cuenca, 2025-09-25) Alvarado Suárez, Kevin Mateo; Armijos Goercke, Santiago Ariel; Granda Juca, María Fernanda; Parra González, Luis Otto
Nowadays, facial recognition—and particularly emotion recognition through facial expressions— has gained importance in various fields, including video games. This research focused on developing an adaptive Flappy Bird-style game prototype aimed at analyzing how automa-tic emotion detection can enhance player engagement by dynamically adapting the gaming experience. The study was conducted in two phases. In the first phase, a non-adaptive pro-totype was created to collect interaction data and facial expressions. A convolutional neural network was trained using the DAiSEE dataset to identify affective states, while DeepFa-ce detected basic emotions. The collected data was compared with the Game Experience Questionnaire (GEQ) to find useful patterns for adaptive design. In the second phase, a new prototype with real-time adaptation mechanisms was built, based on the previously esta-blished relationships. This system adjusted game variables such as speed, difficulty, and visual/auditory stimuli according to the player’s emotional state. Experimental results sho-wed that the adaptive version significantly improved engagement levels compared to the non-adaptive version. These findings demonstrated that integrating emotional recognition techniques with artificial intelligence is an effective approach to enrich human-computer in-teraction and opens new possibilities for designing emotionally responsive video games.
Desarrollo de un asistente virtual conversacional para la capacitación en auto muestreo y detección del VPH
(Universidad de Cuenca, 2025-09-25) Ordóñez Crespo, Christian Stalin; Morocho Zurita, Carlos Villie
Cervical cancer (CC), mainly caused by Human Papillomavirus (HPV), is one of the leading causes of death among women, particularly in rural areas with limited access to healthcare. HPV self-sampling is an effective and accepted alternative, but its adoption faces barriers such as lack of information and guidance. This thesis presents the development of a conversational Virtual Assistant (VA) integrated into an Android mobile app to educate women aged 30 to 65 about self-sampling and sexual and reproductive health. The VA combines a chatbot built with the Rasa framework and a large language model (LLM) to handle queries outside the trained dataset. A dataset of 212 validated question-answer pairs was created, incorporating colloquial language from the target population. Initially, a local LLM was implemented for offline use, but due to performance issues and app size, it was replaced with a rule-based chatbot. Laboratory and final usability tests with women from Baños parish showed high acceptance, with users highlighting the system's clarity, usefulness, and ease of use. The System Usability Scale (SUS) questionnaire yielded an average score of 90.6/100, indicating excellent usability. Overall, the developed system proves to be a viable solution for improving access to reliable medical information in rural settings.
Estudio de la factibilidad de plataformas Low-Code/No-Code en Pymes de Cuenca para la digitalización de procesos administrativos y análisis de datos
(Universidad de Cuenca, 2025-09-24) Muñoz Tufiño, Steven Marcelo; Peñaloza Espinoza, Guido Alexander; Alvear Alvear, Oscar Patricio
This study analyzes the feasibility and impact of Low-Code/No-Code platforms on the digitalization of administrative processes and data analysis in SMEs in Cuenca, Ecuador. Through a literature review, structured interviews, and a case study at the University of Cuenca, the main challenges, needs, and opportunities for technology adoption in local microenterprises and SMEs are identified. The results show that although there is interest in digitalization, barriers such as limited resources, lack of technical knowledge, and absence of clear strategies still persist. Low-Code/No-Code platforms emerge as a viable alternative to overcome these limitations, enabling the agile development of customized solutions, task automation, and data integration without requiring significant investments. The case study demonstrates that, through the use of these tools, it was possible to implement a solution for inventory data entry and visualization in an institutional environment, facilitating report generation and data-driven decision-making, which contributed to improved efficiency and transparency. It is concluded that the adoption of Low-Code/No-Code technologies can accelerate the digital transformation of SMEs, provided it is accompanied by training, support, and proper change management. Finally, recommendations are proposed to strengthen the digital culture and ensure the sustainability of the implemented solutions.
Desarrollo de un Sistema Recomendador para la Asignación de Docentes para asignaturas en la Universidad de Cuenca
(Universidad de Cuenca, 2025-09-24) Quito Urgilés, Pablo Esteban; Valdiviezo Armijos, Juan Javier; Maldonado Mahauad, Jorge Javier
The proper allocation of faculty to courses in higher education represents a key challenge in academic talent management, as it directly affects the quality of the teaching–learning process. Currently, this procedure is often carried out manually, introducing limitations such as subjectivity, lack of standardization, and high administrative workload, which frequently lead to suboptimal assignments. To address this issue, the present study proposes and va- lidates a recommender system designed to optimize the faculty assignment process. The system combines sentiment analysis, through transformer-based language models adapted to the local context, with mathematical models that enable precise alignment between faculty competencies and the specific academic requirements of each course. Faculty profiles were generated by integrating historical evaluations, automatically categorized student feedback, and competencies defined by the institutional Faculty Competency Pentagon. In addition, dynamic weights were incorporated to adjust the relevance of factors according to the aca- demic term, ensuring consistency with institutional expectations. The results demonstrate that the system met the proposed objectives, producing assignments consistent with the cri- teria of program directors in the faculty of engineering. A pilot evaluation with three directors revealed high acceptance and positive assessments of the proposed profiles and assign- ments. Although the limitations of the pilot study prevent broad generalization, the feedback suggests significant potential for application in similar contexts. Overall, the system reduces operational workload and positions itself as a strategic tool with scalability and applicability to other educational environments.
Puesta a prueba de un esquema de teleconsulta con la implementación de videoconferencia confiable
(Universidad de Cuenca, 2025-09-24) Moyano Dután, Juan Gustavo; Morocho Zurita, Carlos Villie
This thesis focuses on the development and evaluation of secure videoconferencing software integrated into a telemedicine system designed to facilitate remote medical consultations. The software complies with HIPAA standards and utilizes WebRTC and Next.js technologies, following the Agile Secure FDD methodology for feature-driven development with enhanced security measures. The research methodology includes a comprehensive review of related literature in e-health. The empirical study on the developed software involved evaluating user perceptions using the TAM methodology through structured questionnaires with statistical analysis and applying the evaluation framework based on the WHO support tool. Key findings indicate that users perceive the software as highly useful and maintain positive attitudes towards it, while also suggesting areas for improvement in ease of use. Additionally, this thesis integrates two previous works by INNTRATEC: one on Digital Identity and another on the integration and interoperability of electronic health records (EHR) using FHIR. Practical experiments included the use of STARLINK satellite communication and a pulse oximeter to enhance remote monitoring capabilities in real-world medical scenarios, confirming the stability, security, and efficiency of the software. The thesis concludes with recommendations to improve the user interface, strengthen user education, and continuously enhance security measures. Future work includes the integration of additional medical devices and improving data storage and analysis capabilities.
Detección de cambios en suelos utilizando imágenes de radar de apertura sintética SAR
(Universidad de Cuenca, 2025-09-23) Calle Siavichay, Mateo Sebastián; Tigre Cajas, Santiago Ismael; Saquicela Galarza, Víctor Hugo
Soil degradation is an increasingly pressing global concern, with direct impacts on agriculture, climate stability, and ecosystem health. In regions such as the Ecuadorian Amazon, this issue is intensified by persistent cloud cover, which hinders consistent land monitoring using optical imagery. This context presents a significant technical challenge, as conventional methods based on optical images depend on favorable atmospheric conditions, limiting their ability to deliver reliable and frequent information in cloud-covered areas. The lack of updated data restricts the early detection of land-use changes and delays decision-making aimed at environmental conservation and sustainable land management. To address this limitation, this study proposes an alternative approach for change detection in land use by employing Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 satellite, which enables surface observation regardless of cloud cover or time of day. A comprehensive process was developed, including image preprocessing, calculation of the RVI index, and the application of deep learning models—highlighting the Bi-temporal Adapter Network (BAN) for its adaptability. The model was retrained using local data and validated through fieldwork. As a final outcome, an interactive visual interface was developed to explore detected changes intuitively, supporting its application in environmental monitoring and land-use planning.
Desarrollo de un Chatbot multiagente basado en modelos LLM y arquitectura RAG para la interacción en lenguaje natural con bases de datos organizacionales
(Universidad de Cuenca, 2025-09-24) Coronel Crespo, Juan José; Ulloa Bernal, Diego Andrés; Saquicela Galarza, Víctor Hugo
In contemporary organizational environments, efficient access to information has become a critical factor for strategic decision making, process optimization, and service quality impro-vement. However, many organizations encounter challenges when querying both structured and unstructured data due to a reliance on specialized technical expertise. This dependency hinders the effective utilization of internal knowledge and limits the democratization of infor-mation access. To address this challenge, the present research proposes the development of a team of agent chatbot with conversational artificial intelligence capabilities, based on the Retrieval Augmented Generation (RAG) architecture and Large Language Models (LLMs). Unlike traditional rule based systems, the proposed solution enables open-ended queries in natural language, facilitating user interaction with databases without requiring technical knowledge. The system was evaluated across multiple architectural configurations including Vanilla RAG, Agentic RAG, and Fine Tuning as well as with several open source LLMs. Among these, the Mistral model stood out for its contextual accuracy. Additionally, techni-ques for the effective transformation of organizational data into semantic representations were validated, and optimal configurations for contextual retrieval and information structu-ring were identified. The results demonstrate the potential of this solution to democratize access to organizational knowledge and serve as a foundation for future advancements in applied artificial intelligence.
Reconocimiento de emociones utilizando señales cerebrales recogidas a través de Interfaces cerebro-computador para la computación afectiva mediante aprendizaje automático
(Universidad de Cuenca, 2025-09-23) Abril Cabrera, Juliana Nicole; Granda Salamea, Camila Verónica; Cedillo Orellana, Irene Priscila; Auquilla Sangolquí, Andrés Vinicio
Affective computing aims to develop systems capable of recognizing and responding empathetically to human emotions, with the goal of enhancing human-computer interaction. In this context, brain signals such as EEG stand out as a more precise and objective means of identifying emotional states, as they are less susceptible to conscious manipulation or subjective bias. However, the practical implementation of emotion recognition models faces significant challenges, including reliance on expensive proprietary software that is difficult to replicate and performs inconsistently across different contexts or devices. Additionally, available public databases vary in terms of channel count, sampling frequency, and acquisition protocols, which hinders model generalization. To address these limitations, this study proposes a Machine Learning model for emotion recognition using EEG signals, based on the open-source OpenBCI Cyton + Daisy device. An experimental protocol was designed involving 37 participants exposed to audiovisual stimuli, with emotional responses labeled using the SAM questionnaire. The EEG signals underwent rigorous preprocessing, followed by training and evaluation of classification models such as SVM, RF, MLP, and Transformers in both subject-dependent and subject-independent scenarios. The results demonstrate competitive and consistent performance, validating the feasibility of this approach as an accessible, reproducible, and low-cost alternative in the field of emotion recognition.
Desarrollo de un algoritmo híbrido metaheurístico para el problema de distribución de planta en las MIPYMES textiles del Ecuador
(Universidad de Cuenca, 2023-09-20) Sotamba Once, Luis Miguel; Sigüenza Guzmán, Lorena Catalina
The Facility Layout Problem (FLP) refers to finding the most effective arrangement of facilities in a factory, considering various aspects. These facilities could include departments, personnel, or machinery, to name a few examples. The FLP is an NP-Hard problem, meaning that there are no algorithms capable of providing an optimal solution in a reasonable polynomial time. Due to this, researchers have turned to metaheuristics and have attempted to combine them to create hybrids. Hybrid metaheuristics can leverage the strengths of each approach to generate higher-quality solutions that are close to optimal. However, many studies that develop hybrid metaheuristics do not use a methodology that explains the development process. For this reason, this work focuses on composing a hybrid metaheuristic following a methodology that encourages the use of SWOT analysis, the Strategic Choice Approach, and Convergent/Divergent Thinking. The hybrid metaheuristic called GENTSA was constructed using a Genetic Algorithm, Simulated Annealing, and Tabu Search. GENTSA solves the FLP that arises in small and medium-sized textile businesses in Ecuador, which was used as a case study. Additionally, as part of its validation, test functions obtained from the state of the art were used. GENTSA was compared to other metaheuristics where it achieves the best facility layout with the lowest cost and reasonably adapts to problem domains beyond its original design scope.
Desarrollo de un sistema integrado de captura, procesamiento y consulta de información nutricional de productos procesados y ultra procesados: una aplicación móvil con asistente virtual basado en RAG
(Universidad de Cuenca, 2025-01-24) León Carrión, José Luis; Quinde Pasato, Henry Miguel; Espinoza Mejía, Jorge Mauricio; Abril Ulloa, Sandra Victoria
The increasing consumption of processed and ultra-processed foods poses significant public health challenges due to its association with non-communicable diseases. This study proposes an integrated system for capturing, processing, and querying nutritional information of these products, specifically tailored for the Ecuadorian context. The system includes a mobile application that utilizes Optical Character Recognition (OCR) and a Named Entity Recognition (NER) model to extract data from product labels, which are then stored in a knowledge graph managed with Neo4j. A web platform enables the validation and management of the captured data, while a virtual assistant based on Retrieval-Augmented Generation (RAG) facilitates natural language queries, generating precise and context-aware responses. The system architecture combines advanced technologies such as Google Vision API, OpenAI, Flutter, and Angular, providing an efficient solution adapted to local needs. Preliminary evaluations demonstrate high levels of usability and accuracy, showing that the system not only automates the collection of nutritional information but also enhances its accessibility and reliability, significantly contributing to data management in this field.
Sistema de Navegación Virtual 360° para un museo con la ayuda de un asistente de ChatBot con IA y recomendaciones personalizadas. Caso de estudio: Museo del Monasterio de las Conceptas de la Ciudad de Cuenca
(Universidad de Cuenca, 2024-10-08) Chocho Rivas, Rommel Javier; Granda Juca, María Fernanda; Parra González, Luis Otto
Museums face the challenge of adapting their spaces and contents to new technologies to improve accessibility and visitor experience. In this context, the need arises to develop virtual navigation systems that allow users to visit museums in a non-presential, immersive way and also to obtain personalized information about their exhibits and main works of art. This degree project, entitled " 360° Virtual Navigation System for a Museum with the Help of an AI ChatBot and Personalized Recommendations. Case study: Museo del Monasterio de las Conceptas de la Ciudad de Cuenca", focuses on the implementation of a computer system that presents a 360° navigation interface and a ChatBot with artificial intelligence. The 360° navigation system allows users to explore the museum's rooms and exhibits virtually, while the ChatBot offers personalized answers and recommendations. In order to present more accurate recommendations, a content-based recommendation system was implemented. The methodology used included Empirically-Based Technology Transfer, which ensures effective collaboration between academia and industry. The evaluation of the system is performed through tests with a set of real users, showing positive results in terms of accuracy of the ChatBot responses and the relevance of the recommendations according to the content visited. In addition, areas for improvement and possible future extensions were identified, such as the expansion of the virtual tour to all the museum's rooms.
Generación de código a partir de requisitos basados en voz. Un análisis multicriterio de herramientas de generación de código basadas en Inteligencia artificial
(Universidad de Cuenca, 2024-10-03) Méndez Espinoza, Cristian Ricardo; Parra González, Luis Otto; Granda Juca, María Fernanda
This research entitled: “Code Generation from Voice-Based Requirements. A multi-criteria analysis of artificial Intelligence-based code generating tools” focuses on selecting, evaluating, and comparing artificial intelligence tools applied to a case study on the registration and management of pets in the city of Cuenca, for the generation of code through voice-based prompts, where they are applied patterns in the inputs provided to obtain more complete results. An exhaustive investigation of the types of existing tools is carried out, in addition to interviews with professionals in the software area where a set of 10 tools are analyzed; The selection criteria are that they generate code, that the tool accepts voice input dictated or cloned, and that it has a free use license. As a result, 4 AI tools are obtained that meet these requirements: ChatGPT, Bing, Gemini and Replit, to which a review of use cases is applied through tests carried out nearly with the prepared prompts, also performing a multi-criteria analysis based on Interface features, voice accuracy, ease of use, functions and compatibility through manual calculation processes and supported by the RStudio tool. Finally, a guide to these 4 resulting tools is provided.
Diseño e implementación de un agente conversacional para optimizar el consumo de medicamentos en adultos mayores en la ciudad de Cuenca
(Universidad de Cuenca, 2023-07-27) Armijos Pulla, Carlos Sebastián; Cambizaca Quinde, Juan Daniel; Espinoza Mejía, Jorge Mauricio; Abril Ulloa, Sandra Victoria
Centered on the need to prevent drug-drug and drug-food interactions among the elderly population in Cuenca, this project presents the development of a conversational agent implemented in a mobile application. The study incorporates a database on drug-drug and drug-food interactions, together with a content-based recommendation algorithm, specifically designed to suggest dietary alternatives in the event of potential interactions between a drug and a food item. The findings, endorsed by evaluations conducted by both nutrition students and field experts, confirm the efficacy of the chatbot in identifying pharmacological interactions and generating appropriate recommendations. These results underline the importance of precision systems that facilitate user interaction, for instance, through conversational agents and recommendation algorithms that provide personalized information to prevent drug interactions; moreover, the evaluation by experts assures the reliability and effectiveness of such systems.
Diseño de una arquitectura de Middleware para la evolución de aplicaciones en el entorno del internet de las cosas (IoT)
(Universidad de Cuenca, 2024-09-23) Soriano Eusebio, Alberto Carlo; Zúñiga Prieto, Miguel Ángel; Palacio Baus, Kenneth Samuel
Smart environments enhance industrial and domestic processes through IoT, optimizing efficiency and sustainability, yet they face challenges in interoperability, modularity, and scalability due to technological diversity and the lack of regulations. Standardizing protocols is key to overcoming these obstacles and facilitating innovation. This paper proposes a software architecture that guides the creation of middleware for interacting with IoT devices. These middleware applications simplify the interaction between IoT devices and applications, enabling efficient implementation and operation in heterogeneous environments. The developed middleware will provide two main benefits: facilitating code implementation and managing device interaction during execution in dynamic environments. A modular architecture based on the microkernel was designed, incorporating design patterns such as Strategy, Factory, and Publish-Subscribe to ensure modularity, scalability, and adaptability in heterogeneous technological environments. Evaluation through a case study demonstrated that this architecture is efficient and effective in addressing IoT challenges, providing a useful framework for future developers and researchers in the creation of IoT applications. In the case study, a software prototype based on this architecture was developed, and tests were conducted that validated its efficiency, scalability, and fault tolerance. The results confirmed that the proposal effectively manages the interaction between IoT applications and devices, meeting high standards of functionality and quality.
Taxonomía orientada a la interacción humano computador (HCI) en sistemas auto-adaptativos
(Universidad de Cuenca, 2024-09-25) Avila Fajardo, José Marcelo; Quito Arévalo, Christian Patricio; Cárdenas Delgado, Paúl Esteban; Cedillo Orellana, Irene Priscila
The digital era has transformed the interaction between humans and technology, driving the development of systems that not only respond to commands but also adapt to the changing needs of users. However, the complexity of these self-adaptive systems (SAs) and the diversity of contexts in which they operate present significant challenges for their effective implementation. Specifically, the lack of reference frameworks to guide the design and evaluation of human-computer interaction (HCI) in these systems limits their ability to enhance user experience and operational efficiency. This thesis directly addresses this gap by developing a specific taxonomy for HCI in SAs, providing a systematic framework to categorize and evaluate research and applications in this field. Additionally, a quality model in the domain of HCI in SAs is proposed, which assesses essential criteria such as functional suitability, performance efficiency, interaction capability, reliability, security, and flexibility. The validation of these proposals is carried out through the evaluation of the taxonomy’s understandability and the applicability of the quality model, using detailed analyses and systematic methodologies to ensure their relevance and effectiveness in software engineering. This approach provides a solid and organized framework for the development of HCI in SAs and new technologies.
Propuesta y validación de un método orientado al diseño de exergames terapéuticos desplegados en ambientes web y móviles.
(Universidad de Cuenca, 2023-07-25) Calle Vásquez, Luis Eugenio; Medina Figueroa, María Daniela; Cedillo Orellana, Irene Priscila; Campoverde Vizhñay, Johanna Lucía
Since the 1950s-1960s, there has been a migration from physical games to the virtual world, all thanks to technological advances. In addition to recreational and entertainment games, categories have emerged focused on more serious goals, such as education, training, and therapy. Within this category, therapeutic exergames have emerged, which combine physical activity with playful and technological elements. Physical therapy, on the other hand, focuses on improving physical function and promoting the rehabilitation of individuals with various medical conditions. In this context, therapeutic exergames have become a valuable tool used by physiotherapists, forming part of their treatment plans, whether to improve muscle strength, flexibility, balance, coordination, etc. from the patients. In the present document, TEXM is proposed, a method for the design of therapeutic exergames intended to be used in web and mobile environments. With the aim of designing therapeutic exergames that help health professionals. To validate the feasibility of the proposed solution, a therapeutic exergame was created following the steps established in the TEXM method. Said exergame was tested with a sample of 10 domain experts. The results obtained in the case study suggest that the proposed method meets the objectives of supporting a therapeutic process established by a physiotherapist.

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