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Slide 1 of 5 Publicación Acceso Abierto
A human-centered cognitive support framework for manual industrial assembly: integrating perception, agentic reasoning, and augmented reality guidance
(The International Journal of Advanced Manufacturing Technology, 2026-07-13) Rodríguez Gasca, Mariannys; Rodríguez Gasca, Efraín Andrés; Macêdo Barbalho, Sanderson César; Grupo de Investigación Automatización Industrial y Control (GAICO)
Manual industrial assembly remains essential in high-variety and customized production, but increasing product and process complexity places substantial cognitive demands on operators. Existing assistance systems, particularly augmented reality-based solutions, improve instruction visualization and task guidance, yet they often remain weakly connected to the real assembly state and limited in reasoning, adaptation, and decision support. This paper proposes a human-centered cognitive support framework for manual industrial assembly that integrates perception, agentic reasoning, knowledge grounding, and augmented reality guidance. The study is informed by a systematic search, bibliometric overview, and literature analysis of 129 Scopus-indexed documents. The analysis shows that augmented reality dominates current cognitive support approaches, while AI-based methods are increasingly used for object recognition, contextual interpretation, adaptive information delivery, and error detection. However, perception, reasoning, guidance, and knowledge grounding are still commonly treated as isolated functions. Based on these findings, 11 review-derived design requirements are formulated and used to develop a conceptual perception-cognition-guidance framework that represents cognitive support as a closed human-centered loop grounded in procedures, constraints, rules, and memory. The framework is then translated into a layered implementation architecture comprising physical, perception, cognitive, guidance, knowledge, and application layers. Structured data contracts clarify how sensory and interaction data can be transformed into perception evidence, structured assembly states, cognitive support decisions, and device-specific guidance commands. A toy-train assembly demonstrator illustrates how procedural state modeling, multi-camera perception, YOLO11-based object detection, projector-based guidance, and contract-based data exchange can connect physical assembly events with structured reasoning and operator-facing feedback.
Slide 2 of 5
Slide 3 of 5 Publicación Acceso Abierto
Gaming in Latin America: An exploration of consumption practices, emotions, motivations, and perceived health impacts
(index.communication Revista científica de comunciación aplicada, 2026-04-18) Treviño González, Raúl Alejandro; Cobos Cobos, Tania Lucía; Grupo de Investigación en Estudios Sociales y Humanísticos- GESH
This article analyzes gaming practices in Latin America from a generational perspective, considering intensity of use, routines, motivations, associated emotions, and self-perceived effects on physical and mental health. The study engages the debate on video games as a service (GaaS) and is based on an online survey conducted between April and May 2024 with 1,304 participants, using convenience sampling and dissemination through digital social networks. Based on descriptive statis-tics, the results show continuities across generations: gaming intensifies during weekends, and age-related differences are moderate, although Generation Z reports higher frequency and intensity. Mo-tivations related to challenge, narrative exploration, aesthetic enjoyment, and stress relief predomi-nate. Most participants do not perceive negative impacts, except for sleep disturbances and visual or musculoskeletal discomfort.
Slide 4 of 5 Publicación Acceso Abierto
Videojugar en América Latina: exploración de prácticas de consumo, emociones, motivaciones y percepciones de impacto en la salud
(index.comunicación Revista científica de comunicación aplicada, 2026-05-18) Treviño González, Raúl Alejandro; Cobos Cobos, Tania Lucía; Grupo de Investigación en Estudios Sociales y Humanísticos- GESH
Este artículo analiza las prácticas de consumo de videojuegos en América Latina desde una perspectiva generacional, considerando intensidad de uso, rutinas, motivaciones, emociones aso-ciadas y autopercepción de efectos sobre la salud física y mental. El estudio dialoga con el debate sobre los videojuegos como servicio (GaaS) y se basa en una encuesta en línea aplicada entre abril y mayo de 2024 a 1.304 participantes, mediante muestreo por conveniencia y difusión en redes sociales digitales. A partir de estadística descriptiva, los resultados muestran continuidad entre generaciones: el juego se intensifica durante los fines de semana y las diferencias por edad son moderadas, aunque la Generación Z presenta mayor frecuencia e intensidad. Predominan motivaciones vinculadas al reto, la exploración narrativa, el disfrute estético y el desestrés. La mayoría no percibe impactos negativos, salvo alteraciones del sueño y molestias visuales o musculoesqueléticas.
Slide 5 of 5 Publicación Acceso Abierto
Development of a remaining useful life prediction model for marine diesel engine filtration systems
(International Journal of Prognostics and Health Management, 2026-06-14) Suarez Loaiza, Joan Suarez; Guimarães de Paula, Clara Olimpia; Paipa, Edwin; Martínez Santos, Juan Carlos; Puertas Del Castillo, Edwin Alexander; Grupo de Investigación Tecnologías Aplicadas y Sistemas de Información (GRITAS); Semillero de Investigación en Inteligencia Artificial
Marine diesel propulsion engines are essential to naval platforms, enabling maneuvering, navigation readiness, and training operations. However, maintenance of propulsion consumables—particularly fuel filtration elements—often remains time-based and corrective despite the growing availability of onboard operational records. This paper presents the development and validation of a Remaining Useful Life (RUL) prediction model for the propulsion engine filtration system of the Colombian Navy (ARC) training ship, aiming to estimate time to replacement for cartridge-based filters.
The proposed approach handles imperfect manual operational data and scarce, non-uniform maintenance labels through a Prognostics and Health Management (PHM) workflow guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM). It combines physics-informed data quality control using plausibility bounds, outlier mitigation, and time-series reconstruction; expert validation of representative operating cycles using a Delphi protocol; and event logging to align filter-replacement actions with gap-aware approximations. It was trained supervised regression models using an automated machine learning (AutoML) strategy implemented in PyCaret and refined through hyperparameter optimization in Optuna. A Random Forest model achieved the best performance, reaching a test root mean squared error (RMSE) of 52.92 hours with a coefficient of determination of 0.921.
