This study investigates visual fatigue in a real-world pharmaceutical packaging environment, where operators perform repetitive inspection and packing tasks under frequently suboptimal lighting conditions. A human-centered methodology was adopted, combining adapted self-report questionnaires, high-frequency eye-tracking data collected with Tobii Pro Glasses 3, and lux-level measurements. Key eye-movement metrics—including fixation duration, visit patterns, and pupil diameter—were analyzed within defined work zones (Areas of Interest). To reduce data complexity and uncover latent patterns of visual behavior, Principal Component Analysis was applied. Results revealed a progressive increase in visual fatigue across the workweek and throughout shifts, particularly during night work, and showed a strong association with inadequate lighting. Tasks involving high physical workload under poor illumination emerged as critical risk scenarios. This integrated approach not only confirmed the presence of visual fatigue but also identified high-risk conditions in the workflow, enabling targeted ergonomic interventions. The findings provide a practical framework for improving operator well-being and inspection performance through sensor-based monitoring and environment-specific design enhancements, in alignment with the goals of Industry 5.0.

La valoración del riesgo biomecánico en las actividades industriales es una estrategia que permite la toma de decisiones oportunas en la prevención de aparición de enfermedades osteomusculares en la población trabajadora; sin embargo, los métodos de evaluación tradicionales no permiten hacer una valoración ágil y rápida, siendo procesos que demandan tiempo y experticia del evaluador en ocasiones con resultados sesgados según el criterio del observador. El presente artículo presenta el estado del arte en evaluación de métodos de detección automatizada de riesgo ergonómico en entornos de trabajo industrial, dentro del marco del proyecto de investigación desarrollo de un modelo predictivo de evaluación del nivel de riesgo biomecánico en miembros superiores, en la industria farmacéutica, aplicando técnicas de IA, según metodología OCRA.

El propósito de este artículo es evaluar el nivel de madurez digital en los sistemas administrativos de los municipios colombianos. Para lograr esto, se utilizó una metodología mixta que incluyó la aplicación de una encuesta estructurada a una muestra representativa de 235 municipios en el país. Los resultados obtenidos revelan un panorama variado: aunque se han realizado avances en áreas como el liderazgo digital y la seguridad cibernética, todavía existen brechas significativas en aspectos críticos como la infraestructura tecnológica, la automatización de procesos y el análisis de datos. Entre los hallazgos más importantes se destaca la necesidad de fortalecer las inversiones en capacidades técnicas, acelerar la integración de sistemas para mejorar la eficiencia operativa, desarrollar el aprovechamiento de información para tomar decisiones y fortalecer los servicios digitales dirigidos a los ciudadanos. En conclusión, la transformación digital en la gestión pública territorial está en marcha en Colombia, pero enfrenta desafíos importantes. Es prioritario abordarlos mediante una estrategia integral y un compromiso conjunto entre directivos, funcionarios y otros actores para aprovechar al máximo los beneficios que ofrece la era digital en la administración municipal.

This study investigates visual fatigue in a real-world pharmaceutical packaging environment, where operators perform repetitive inspection and packing tasks under frequently suboptimal lighting conditions. A human-centered methodology was applied, combining adapted self-report questionnaires with high-frequency eye-tracking data collected via Tobii Pro Glasses 3, alongside lux-level measurements. Key eye movement metrics, such as fixation duration, visit patterns, and pupil diameter, were analyzed within defined work zones (Areas of Interest). Principal Component Analysis was employed to reduce data complexity and uncover latent visual behavior patterns. Results revealed a progressive increase in visual fatigue across the workweek, throughout each shift, and particularly during night shifts, and is strongly associated with inadequate lighting. Notably, tasks involving high physical workload under poor illumination emerged as critical risk scenarios. This integrated approach not only confirmed the presence of visual fatigue but also identified high-risk conditions in the workflow, enabling targeted ergonomic interventions. The findings offer a practical framework for enhancing operator well-being and inspection performance through sensor-based monitoring and environment-specific design improvements, aligned with the goals of Industry 5.0.

This study presents OCRA-AI, a semi-automated computer vision system for biomechanical risk assessment based on the OCRA Checklist methodology. The system integrates three deep learning models — MediaPipe Hands for hand landmark estimation, YOLO-Pose for proximal upper-limb joint detection, and YOLO-grips, a custom-trained model for classifying industrial grip types — and was evaluated in 43 pharmaceutical workers performing repetitive packaging tasks in a real industrial environment.,The YOLO-grips model achieved a mean Average Precision at IoU = 0.5 (mAP@0.5) of 0.804, with per-class performance ranging from 0.671 (left hook grip) to 0.887 (right hook grip). OCRA-AI exhibited laterality-dependent performance. For the non-dominant left upper limb, the system demonstrated moderate concurrent validity with the manual OCRA Checklist (Spearman ρ = 0.50, p < 0.001; ICC(2,1) = 0.34), with near-zero systematic bias in the Bland–Altman analysis (bias = +0.12 ICKL units; MAE = 1.93; MAPE = 11.5%). For the dominant right upper limb, agreement was poor (Spearman ρ = -0.10, p = 0.544; ICC(2,1) = -0.10) despite low absolute errors (MAE = 0.97; MAPE = 5.4%), primarily reflecting the model's convergence toward the population mean. Work shift showed a significant positive association with left-limb risk (ρ = +0.35, p = 0.021), with the highest risk observed during the night shift. Overall, the system reduced assessment time by 93% (28.4 to 2.1 minutes per worker).,OCRA-AI represents a scalable tool for group-level ergonomic surveillance in occupational settings. Individual-level classification, particularly for dominant-limb assessment, still requires expert ergonomist judgment or complementary reference instrumentation.