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A hybrid bio-inspired visual servoing strategy for aerial grasping of static and moving targets
(2026)
Carvajal, Christian
;
Víctor H. Andaluz
;
Flavio Roberti
;
Varela Aldas, José
;
Ricardo Carelli
This article presents a bio-inspired visual servo control scheme for aerial grasping of static and moving targets using aerial manipulators (AM). The proposed approach is based on a cascaded control architecture. The first control layer implements a bio-inspired hybrid visual servo strategy that combines Image Based Visual Servoing (IBVS) and Position Based Visual Servoing (PBVS), motivated by the prey capturing behavior of birds of prey. An Aerial-Eye-to-Hand visual configuration is introduced, in which an onboard camera mounted on the aerial platform is used to simultaneously regulate image plane features and spatial variables associated with the target. The grasping task is organized into four autonomous stages, enabling coordinated detection, approach, grasping, and transportation. Exploiting the redundancy of aerial manipulators, the proposed scheme allows the simultaneous fulfillment of multiple control objectives, including target visibility maintenance and accurate end effector positioning. The second control layer consists of a dynamic compensation controller formulated in terms of reference velocities, which facilitates its implementation on aerial platforms with embedded low level controllers. A formal stability and robustness analysis of the cascaded closed-loop system is provided. Experimental results, including real-world aerial grasping of moving ground targets in unstructured environments, demonstrate the effectiveness of the proposed approach. © 2026 The Author(s)
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Robust Visual Servoing for Quadruped Robots: ISS-Based Target Tracking With Secondary Formation Objectives
(2026)
Carvajal, Christian
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Chicaiza Claudio, Fernando
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Varela Aldas, José
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Víctor H. Andaluz
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Flavio Roberti
This workpresents the design, implementation, and experimental validation of a visual servo control scheme formulated at the task-space level and applied to a quadruped robot equipped with an active two degree of freedom (2DoF) vision system for continuous tracking of moving targets. The proposed controller is based on a differential kinematic model that maps the robot's task-space velocity references to the kinematic evolution of the active vision system, incorporating a target velocity estimator that enables dynamic compensation of the target motion projected onto the image plane. The control architecture exploits the kinematic redundancy of the robotic system to simultaneously address multiple control objectives within a hierarchical framework. The primary task ensures target centering in the image plane, thereby maintaining visibility within the robot's field of view, while the secondary task regulates relative formation by controlling the distance and orientation between the robot and the target. Quadruped locomotion is handled by a low-level gait controller and is not modified by the proposed scheme. Closed-loop stability is rigorously analyzed using Lyapunov theory, demonstrating Input-to-State Stability properties in the presence of bounded disturbances. Simulation and real-world experimental results obtained with the Unitree Go2 robot confirm the robustness and stability of the proposed approach for dynamic visual tracking in realistic scenarios. © 2020 IEEE.
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Recent Advances in Multi-Camera Computer Vision for Industry 4.0 and Smart Cities: A Systematic Review
(2026)
Fierro Silva, Carlos Julio
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Carolina Del-Valle-Soto
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Samih M. Mostafa
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Varela Aldas, José
The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures.
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Stochastic Characterization of MAC-Level Reliability and Reassociation Dynamics in IEEE 802.15.4 Networks for Smart Grid Applications
(2026)
Carolina Del-Valle-Soto
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José A. Del-Puerto-Flores
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Ramiro Velázquez
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Juan Sebastián Botero-Valencia
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Leonardo J. Valdivia
Wireless communication networks based on IEEE 802.15.4 and ZigBee PRO constitute a critical component of smart grid infrastructures, where reliability and availability requirements exceed those typically assumed in low-power wireless deployments. Despite extensive analytical modeling, most existing studies rely on independence assumptions for packet errors and simplified abstractions of reassociation dynamics. This work presents stochastic reliability characterization grounded on real MAC-layer traffic capture from an operational IEEE 802.15.4/ZigBee PRO network. The methodology combines statistical hypothesis testing, first-order Markov modeling, spectral-gap analysis, large-deviation theory, renewal processes, and survival analysis of realignment intervals. Empirical results reject the hypothesis of independent frame errors and demonstrate significant temporal dependence with geometric mixing behavior. The estimated transition structure reveals burst-error persistence, inflating long-run variance relative to memoryless models. Furthermore, coordinator realignment intervals deviate from exponential behavior, exhibiting non-constant event rates consistent with regenerative dynamics. These findings indicate that effective communication reliability is governed not only by average frame error probability but also by dependence structure and regeneration mechanisms. The proposed probabilistic framework provides a rigorous and reproducible methodology for dependence-aware reliability assessment in smart grid communication systems.
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Immersive Virtual Reality Application for the Evaluation of Depression in Older Adults
(2026)
Buele, Jorge
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Varela Aldas, José
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Labre Tarco, Verónica
Although evidence supports the use of virtual reality for cognitive assessment in the older adult, studies on the assessment of mental disorders such as depression are scarce. Previous studies show satisfactory results in the young adult population, but further analysis is needed in the geriatric population. This research analyzed the ability of a virtual reality application to assess levels of depression in older adults, analyzing the correlation between demographic variables and performance on the application. Fourteen older adults with an age of 74.86 (5.4) participated in the study. Demographic variables, depressive symptoms were evaluated, and Spearman correlation tests were performed to analyze the relationship between age, schooling and performance in the application. The research reveals a significant association between age and task execution time, indicating that the older the age, the longer it takes to complete the task. A correlation is identified between schooling and the number of errors, highlighting that more education does not guarantee the absence of errors. Although no direct correlation was found between level of depression and application performance, the promising utility of virtual reality in this area, supported by recent studies, is underscored. The study contributes to the understanding of how virtual reality applications can be valuable in the assessment of mental health in older adults. Although limitations such as sample size are acknowledged, the results establish a foundation for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Electrodermal Response Patterns and Emotional Engagement Under Continuous Algorithmic Video Stimulation: A Multimodal Biometric Analysis
(2026)
Carolina Del-Valle-Soto
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Violeta Corona
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Jesus GomezRomero-Borquez
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David Contreras-Tiscareno
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Diego Sebastian Montoya-Rodriguez
Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments.
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Analysis of Urban Transport User Satisfaction in Ambato: Segmented Results from a Target Population
(2025)
Junta, Christian
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William Avila-Armijos
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Núñez-Naranjo, Aracelly Fernanda
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Varela Aldas, José
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Fernando Vladimir Junta
Urban public transportation plays a crucial role in the daily mobility of citizens, especially in medium-sized cities like Ambato, Ecuador. This study aims to evaluate user satisfaction levels with different urban transport cooperatives through a data-driven approach. A total of 123 responses were collected via a digitally distributed survey using QR codes, with a methodological focus on individuals aged 20 to 40 due to their higher digital literacy. The questionnaire included dimensions such as comfort and safety, risk perception, and punctuality. The analysis, conducted through Power BI, revealed differentiated levels of satisfaction across service dimensions. Comfort and safety received a mean rating of 41.5%, while risk perception reached 53.8%. Punctuality, evaluated through a single item, showed a favorable perception from 66.7% of users. These findings indicate specific areas of concern, such as perceptions of security and cleanliness, which require attention from transport providers and local authorities. The results provide a scalable model for user satisfaction assessment that can be replicated in other urban contexts. Moreover, the integration of business intelligence tools facilitates evidencebased decision-making and supports future initiatives to enhance urban mobility systems in Ecuador and Latin America. © 2025 IEEE.
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Experimental Validation of a Kinematic Control Strategy for Trajectory Tracking in Quadruped Robots
(2025)
Carvajal, Christian
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Varela Aldas, José
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Víctor H. Andaluz
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Chicaiza Claudio, Fernando
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Ayala-Chauvin, Manuel Ignacio
This work presents a high-level control architecture for trajectory tracking in quadruped robots. The proposed controller is based on the motion kinematics of the robot's center of mass (CoM). The proposed strategy transforms planned trajectories in Cartesian space into motion velocity commands for the robot, using a differential kinematic model that relates the velocity of the robot's operational point to its velocity in the XY-plane. The control scheme is organized hierarchically, where the kinematic controller operates independently from the system dynamics, which are handled by low-level controllers. The proposed control architecture is experimentally validated using the Unitree Go2 quadruped robot, employing MATLAB and ROS2 tools. The results confirm the feasibility of using purely kinematic models for high-level locomotion task control under real-world operating conditions. © 2025 IEEE.
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A Literature Review on Real-Time Crime Detection Using Deep Learning and Edge Computing
(2025)
Carlos Julio Fierro Silva
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Carolina Del-Valle-Soto
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Varela Aldas, José
The growing need for security in urban and commercial environments has driven the development of intelligent surveillance systems capable of detecting criminal activities in real time. While traditional cloud-based solutions offer advanced capabilities, they face limitations in terms of latency, privacy, and bandwidth usage. In this context, Edge Computing has emerged as a promising alternative, enabling local and fast processing of video data through embedded artificial intelligence models. This review article presents a comprehensive analysis of recent advances in real-time detection of thefts and weapons using Edge Artificial Intelligence (Edge AI). A total of 30 scientific articles published between 2018 and 2025 were selected and categorized, taking into account detection models, computing platforms, evaluation metrics, datasets, and real-world applications. The results highlight the predominant use of lightweight convolutional neural networks, especially YOLO-based models, implemented on devices such as Jetson Nano, Raspberry Pi, and Google Coral. Key challenges addressed include detection under lowlight conditions, identification of small or partially concealed weapons, and the reduction of false positives. The review identifies gaps in the current literature, such as the lack of annotated real-world datasets and the need for behavior-based models in retail contexts. Finally, emerging trends and future research directions are discussed, aiming at the development of efficient, accurate, and privacy-respecting Edge AI systems for real-time security surveillance. © 2025 IEEE.
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A review on the rehabilitation of central auditory processing disorder using haptic interfaces and virtual environments
(2025)
Erick Pule-Ponce
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Varela Aldas, José
This article presents a review of the literature on the promise of integrating haptic interfaces and virtual environments as tools for the rehabilitation of Central Auditory Processing Disorder (CAPD). These technologies provide complementary sensory information that improves auditory localization and allows controlled scenarios to be created for more effective training. The integration of artificial intelligence has shown the ability to evaluate automated speech and detect communication disorders, facilitating the creation of specific neural networks during audiovisual integration. However, there are significant challenges such as high development costs, the need for specialized training of healthcare personnel, individual variability in therapeutic responses, and ethical considerations related to data privacy. Future perspectives point towards convergent technology platforms that integrate multiple sensory channels, more ergonomic interfaces and more personalized medicine requiring interdisciplinary collaboration for the development of effective integrated solutions. © 2025 IEEE.
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