Centro de investigación en Mecatrónica y Sistemas Interactivos

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Universidad Indoamérica

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Immersive AR–ROS 2 Teleoperation Architecture for a Physical Quadruped Robot
(MDPI AG, 2026-07-15)
Criollo, Erick
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Oñate, William
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Caiza, Gustavo
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Andaluz, Víctor
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Varela Aldas, José
Immersive teleoperation of quadruped robots requires the operator to interpret a remote environment, make decisions, and maintain control over a dynamic platform through mediated visual feedback and networked command transmission. This study presents and validates a reproducible augmented reality (AR)–ROS 2 architecture designed to analyze the relationship between system-level technical conditions and operator experience during immersive teleoperation of a physical quadruped robot. The system integrates Meta Quest 3, Unity, UDP communication, ROS 2 middleware, and Xiaomi CyberDog within a modular workflow that jointly supports immersive control, live RGB/depth perception, physical robot execution, and user-centered HRI evaluation. The architecture decouples command transmission and visual feedback into two UDP-based channels: the control channel maps virtual joysticks and discrete HUD buttons to ROS 2 locomotion and action commands, while the perception channel transmits compressed RGB and depth frames for display in the AR interface. Under nominal conditions, the control channel achieved a mean end-to-end latency of 9.37 ms, a P95 of 18.45 ms, an effective update frequency close to 18 Hz, and 100% packet reception. Compared with ROS-TCP-Endpoint, the proposed UDP bridge showed similar latency but higher flow integrity, avoiding duplicate, parsing, and dropped-command events. The visual channel achieved mean end-to-end latencies of 21.09 ms for RGB and 26.99 ms for depth, with frame reception rates of 91.78% and 91.10%, respectively, under a shared 18.67 Mbps mobile network. A complementary network degradation analysis showed that the control channel remained operational under bandwidth reduction, packet loss, added latency, combined degradation, and physical separation of the robot. The user evaluation with 25 participants yielded a corrected Raw NASA-TLX score of (Formula presented.), a positive perceived performance score of (Formula presented.), and a System Usability Scale score of (Formula presented.), indicating low perceived workload and high usability. These results show that low end-to-end latency, high flow integrity, and stable visual feedback were accompanied by low perceived workload and high usability, indicating that the technical temporal response, stability, and robustness of the communication channels are consistent with an effective and low-demand operator experience during AR-based teleoperation of a physical quadruped robot.
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ROS2-Based Low-Cost Mobile Robot for Educational Assistance with Reactive Navigation and Semantic-Cached Language Processing
(MDPI AG, 2026-07-08)
Aucapiña, Sebastián Alexis
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Benalcázar, Nataly Cecilia
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Varela Aldas, José
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Isa-Jara, Ramiro
Educational environments, particularly those with limited resources, require affordable mobile robots capable of combining human–robot interaction, autonomous assistance, and academic support without continuous dependence on cloud services. This work presents a low-cost ROS2-based mobile robot implemented on a Raspberry Pi 4B to provide educational assistance in Spanish within controlled classroom environments. The system integrates voice interaction, text-to-speech synthesis, YOLOv8n-based object perception, a specialized door detection model, ultrasonic and inertial sensing, differential-drive control, and a hybrid natural language processing architecture based on semantic caching, local inference, and optional cloud connectivity. Two task-dependent operating modes, education and navigation, selectively activate ROS2 nodes to reduce computational load and energy consumption. Experimental tests conducted in a university classroom evaluated speech recognition, vision models, natural language processing alternatives, sensor behavior, and battery life. The speech recognition module achieved 98% accuracy under both quiet and noisy conditions. YOLOv8n achieved an F1-score of 0.975 for common classroom objects, while the specialized door detector achieved 100% recall with 58.7% precision. The semantic cache correctly resolved recurrent academic queries in the exact-match evaluation, with an average latency of 3.8 s, reducing the need for external language models in known-question scenarios. The robot operated for 96 min in education mode and 75.6 min in navigation mode. These results demonstrate that Spanish voice interaction, reactive navigation, academic question answering, and resource-aware operation can be integrated into a single low-cost edge robotic platform for educational environments. © 2026 by the authors.
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From simulation to reality: a learning methodology for understanding control systems and robotic arms with the ARCHIE robot
(Frontiers Media SA, 2026-04-10)
Saeteros, Juan
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Mendoza, Erick
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Ramirez, Jefferson
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Fajardo-Pruna, Marcelo
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Buele, Jorge
Bridging the gap between theoretical concepts and real-world implementation remains a critical challenge in the teaching of control systems and robotics. This paper presents an education method using ARCHIE, a low-cost, open-source articulated robot with six degrees of freedom, as an educational platform designed to enhance students’ understanding of control theory through practical learning. The study combines simulation (using MATLAB’s Simscape) with hands-on practice using the physical robot, following a Sim2Real methodology. A two-hour workshop was conducted with undergraduate mechatronics engineering students, incorporating pre- and post-assessments to evaluate learning gains. Preliminary results suggest a statistically significant improvement in students’ understanding of PID control concepts, with average test scores rising from 46.6% to 63.3% (p = 0.0454, two-tailed). The study also highlights the benefits of active learning, allowing students to experience real-world control system behavior and reflect on discrepancies between simulations and physical implementation. ARCHIE proves to be a valuable educational tool for fostering deeper comprehension of control systems in engineering curricula. © 2026 Saeteros, Mendoza, Ramirez, Fajardo-Pruna, Buele, Yumbla and Saldarriaga.
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Advances in end-user water conservation technologies: a scoping review
(Frontiers Media SA, 2026-04-15)
Fernández-Vizcarra, Luis
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Chambilla-Zuñiga, Josué
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Gutiérrez-Aguilar, Olger
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Buele, Jorge
Introduction Water scarcity, intensified by climate change, urbanization, and increasing demand, requires sustainable strategies aimed at reducing consumption among end users. In this context, this article develops a scoping review to examine what types of sustainable technologies aimed at water conservation among end users have been investigated, how they are evaluated, and what types of evidence are reported regarding their effectiveness. Methods Studies published between 2020 and 2025 were retrieved from the Scopus, Web of Science, IEEE Xplore, Wiley Online Library, and EBSCO Engineering Source databases following PRISMA-ScR guidelines. The review integrates a bibliometric analysis of the field and a synthesis of 14 selected empirical studies addressing domestic efficiency technologies, adoption dynamics, digital monitoring systems, and resource management approaches. Results The evidence shows high methodological diversity and limited standardization of effective metrics, as only a small proportion of studies explicitly report quantified or directly measured water savings. Nevertheless, consistent patterns emerge depending on the type of intervention. Circular water management and decentralized reuse strategies report the highest potential impacts, with potable water savings ≥30% through greywater treatment and reductions of up to 80% in integrated urban scenarios. Rainwater harvesting and green infrastructure report runoff reductions between 58.3 and 94.1%, generating indirect benefits for urban water management. In contrast, informational and behavioral interventions tend to produce moderate but consistent reductions in consumption. Digital technologies based on IoT, smart metering, and advanced analytics demonstrate strong technical performance in monitoring and leak detection, although they rarely quantify post-implementation water savings. Conclusion The findings suggest that effective water conservation among end users depends on approaches that combine technological solutions, user engagement, and standardized evaluation frameworks capable of linking technological performance with verifiable reductions in water consumption.
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Fiber–matrix interaction governs compressive strength in agave-bagasse-reinforced adobe: a factorial experiment with two-way ANOVA and competing mechanism analysis
(Frontiers Media SA, 2026-07-31)
De-Obaldia-Escalante, Marcela
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Del-Valle-Soto, Carolina
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Acevedo-Parra, H. R.
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Montoya-Márquez, Orlando
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Varela Aldas, José
Natural-fiber reinforcement is widely cited as a pathway to improve the mechanical performance of adobe, but reported effects on compressive strength are inconsistent across studies: some find improvement, others find degradation, and the choice of experimental conditions rarely disentangles the role of the fiber from that of the matrix. This study quantifies the coupling through a balanced factorial experiment. Forty-nine adobe specimens of (Formula presented) cm were manufactured with three granular compositions (sand-dominated, jal-dominated, and balanced, where jal is a regional non-plastic silt of Jalisco, Mexico) and four mass fractions of agave-bagasse fiber (0%, 0.5%, 1%, and 2%), and were tested under Mexican standard NMX-C-036-ONNCCE by an accredited external laboratory. Three complementary analytical tools are applied to the resulting dataset: (i) a two-way analysis of variance (ANOVA), (ii) a reinforcement efficiency index (Formula presented) with bootstrap confidence intervals, and (iii) a competing mechanism phenomenological descriptor (Formula presented) that separates a saturating reinforcement term from a linear disruption term. The two-way ANOVA reveals a highly significant mixture–fiber interaction ((Formula presented), (Formula presented), and partial (Formula presented)), which is stronger than either main effect and statistically demonstrates that the sign of the fiber effect is not an intrinsic property of the fiber but rather a property of the fiber–matrix pair. For sand-containing mixtures, the reinforcement efficiency index is (Formula presented) [M1, 95% bootstrap CI (0.96, 1.32)] and (Formula presented) [M3, (0.92, 1.59)] at the optimum (Formula presented); a non-parametric bootstrap over 5, 000 resamples places the optimum at (Formula presented) with posterior probability (Formula presented) (M1) and (Formula presented) (M3). For the jal-dominated mixture, fiber inclusion is net destructive [(Formula presented), (0.68, 0.95) at (Formula presented)], with Welch (Formula presented)-tests rejecting equivalence with the control at (Formula presented) (0.5%) and (Formula presented) (2%) and Cohen’s effect sizes (Formula presented). The best-performing conditions yield mean compressive strengths of 3.22 MPa, which exceeds the 2.0 MPa minimum required by NMX-C-441-ONNCCE-2011 for non-structural masonry by 60%. An immersion test shows that unstabilized specimens disintegrate within 2–3 min, bounding applications to non-exposed or externally protected uses and defining the primary direction for future work.
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Low-Cost IoT System with Containerized AI and Telegram Bot for Real-Time Air Quality Risk Communication and Preventive Behavior Change
(Springer Nature Switzerland, 2026)
Zapata, Mireya
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Álvarez-Tello, Jorge
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Rugel-Sanchez, Keyla
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Vargas-Bustamante, Miguel
Air pollution constitutes one of the main environmental risk factors for public health, particularly in urban environments with limited real time monitoring infrastructure. Although low-cost IoT architectures have emerged as scalable alternatives to extend the spatial coverage of measurements, many implementations lack statistically validated risk classification models capable of translating the environmental data into information to service the public. This study presents the development and statistically validates in real time a risk index for air quality, implemented though a low-cost IoT architecture which integrates supervised artificial intelligence (AI) models deployed at the edge. The system was implemented for eight weeks the Universidad de Guayaquil campus, taking records of PM2.5, PM10 concentrations, temperature and humidity using calibrated sensors. The classification model based on Classification and Regression Trees reached a global accuracy greater which surpassed 90%, with 91.7% concordance to data retrieved from official stations for moderate conditions of PM2.5. K-fold method was used during the validation process and direct comparison with certified infrastructure. Additionally, user evaluation (n = 85) showed 82% adoption of preventive behavior after the implementation of proactive communication with a conversational bot. The results show that the integration of the presented low-cost IoT with validated risk models and user focused communication can generate reliable environmental intelligence and supports preventive decision-making in urban contexts with infrastructure constraints.
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Does the use of dedicated mobile devices in magnetism classes improve student learning?
(Frontiers Media SA, 2026-04-21)
Varela Aldas, José
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Collay, Washington
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Del-Valle-Soto, Carolina
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Palacios-Navarro, Guillermo
Mobile devices have been evaluated from multiple perspectives as tools to support classroom learning; however, devices specifically dedicated to learning activities still require further research to elucidate their influence in the classroom. This study aimed to evaluate whether the use of dedicated mobile devices for teaching basic magnetism content improves learning among secondary school students. A quantitative, comparative quasi-experimental study with non-equivalent groups was conducted ((Formula presented)) with secondary school students in Ecuador. The experimental group used an educational application running on a dedicated mobile device (M5Stack Core2) and interacted with an electromagnet as a didactic peripheral, whereas the control group received a traditional teacher-centered lesson supported by slides. Learning outcomes were measured using a teacher-designed theoretical test administered at the end of a 90-min session. Mean scores were similar between groups (experimental: 7.32; control: 7.58). After verifying assumptions of normality and homogeneity of variance, an independent-samples t-test showed no statistically significant differences ((Formula presented)), and the effect size was small (Cohen’s (Formula presented)). Under the conditions evaluated, the dedicated mobile device did not produce measurable gains in knowledge performance compared to the traditional lesson. Longer interventions, larger samples, and longitudinal study designs are needed to clarify when and how this technology may provide educational benefits.
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Addressing the Accessibility Gap in Biometric Security: A Low-Cost, Open-Source Approach for High-Risk Environments
(Springer Nature Switzerland, 2026)
Vargas-Bustamante, Miguel
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Zapata, Mireya
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García-Muñoz, Gabriel
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Álvarez-Tello, Jorge
Facial recognition has become established as a key technology for access control in high-insecurity contexts; however, its adoption in resource-constrained environments remains limited. This study addresses the existing gap in the empirical validation of low-cost biometric systems deployed in real-world scenarios. A prototype access control system based on facial recognition was developed using low-cost embedded hardware and open-source software. The research followed a quantitative, experimental, and applied approach, evaluating system performance using standardized biometric metrics (accuracy, FAR, and FRR) under different environmental conditions. The system achieved 97% accuracy in authorized access attempts and 96% accuracy in unauthorized attempts, with an average latency of 1.8 s and complete resistance to basic spoofing attacks. The results demonstrate that low-cost biometric solutions can achieve acceptable levels of reliability and usability for access control in high-insecurity contexts, contributing to the democratization of technological security
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A Multi-Camera Temporal Fusion for False Alarm Suppression in Edge-Based Weapon Surveillance
(Institute of Electrical and Electronics Engineers (IEEE), 2026)
Fierro Silva, Carlos Julio
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Del-Valle-Soto, Carolina
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Mostafa, Samih M.
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Karim, Faten Khalid
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Varela Aldas, José
Real-time weapon detection is a critical component of intelligent surveillance systems, particularly for perimeter monitoring applications on embedded edge platforms. However, reliable alarm generation remains challenging because false positives, temporal instability, and viewpoint inconsistencies can propagate through conventional multi-camera fusion strategies. To address these limitations, this work proposes a lightweight Adaptive Multi-Camera Temporal Fusion (ACTF) framework that combines confidence-aware evidence separation, temporal persistence, and short-window cross-camera validation at the decision level, thereby confirming detections without requiring additional neural-network inference. The framework was evaluated using a TensorRT-optimized YOLO26s detector in controlled dual-camera scenarios involving clear visibility, partial occlusion, visually ambiguous distractors, and challenging illumination. While logical OR fusion achieved higher recall, it also propagated erroneous detections; in contrast, ACTF completely suppressed the distractor-induced false alarms while maintaining competitive performance and sub-second confirmation under favorable conditions. The original NVIDIA Jetson Nano implementation achieved an average throughput of 2.4 camera-pair cycles per second, corresponding to low-rate online embedded operation, whereas an additional NVIDIA Jetson Xavier NX benchmark achieved an average of 10.2 camera-pair cycles per second. This is equivalent to 10.2 processed frames per second for each camera stream and 20.4 camera images per second in aggregate. These results support reactive real-time embedded operation on the Xavier NX platform and demonstrate that ACTF improves alarm reliability with negligible decision-level computational overhead. © 2013 IEEE.
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Using Predictive Modeling to Assess Social Media's Impact on Parental Well-Being: A Pilot Study
(Springer Nature Switzerland, 2026)
Tutillo, Jimena
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Martinez, Danilo
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Zapata, Mireya
Social media is part of daily life, facilitating communication, entertainment, and immediate access to information. However, it can negatively affect personal well-being and contribute to the development of addictive behavior. The objective of this paper is to examine how social media users in the economically productive age group of 36 to 56 years are affected by these platforms, and how machine learning (ML) and app development can be used to mitigate the impacts of these dopamine-stimulated behaviors. For this purpose, an empirical study using the Social Media Disorder Test showed that 37.5% of the sample had prolonged usage. To aid in the solution, an app was developed that uses machine learning (ML) to classify addiction levels into 8 categories across 3 social media apps. The app monitors usage in real time and issues alerts for excessive consumption patterns to reduce digital dependence. The results show real patterns of moderate addiction among users. It also seeks to raise awareness of social media use so they can set limits and reduce the impact of their use

Centro de investigación en Mecatrónica y Sistemas Interactivos

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