Centro de investigación en Mecatrónica y Sistemas Interactivos

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Universidad Indoamérica

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Gestalt prototyping framework applied to design a mobile-commerce interface
(2021)
Daniel R.
;
Cesar G.
;
Alejandra G.
This article presents the different concepts that were considered to design the interface of an e-commerce platform and its consequent adaptation to a mobile environment. The parameters considered during the development of the high and low fidelity prototypes are the result of an association between usability heuristics, Gestalt principles, and specific user experience (UX) interactions. This project shows the results of the projections of the usability tests carried out on the functional application, in which it was possible to understand the close relationship between the use of usability concepts that intervene in the functional actions of the application with the perception of aesthetics. © 2021 IEEE.
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Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios
(2023)
Guffanti, Diego
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Lemus D.
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Vallery H.
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Brunete A.
;
Hernando M.
Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, assessment of gait consistency requires testing over a longer walking distance. The aim of this study is to validate the accuracy for gait assessment of a previously developed method that determines walking spatiotemporal parameters and kinematics measured with a 3D camera mounted on a mobile robot base (ROBOGait). Walking parameters measured with this system were compared with measurements with Xsens IMUs. The experiments were performed on a non-linear corridor of approximately 50 m, resembling the environment of a conventional rehabilitation facility. Eleven individuals exhibiting normal motor function were recruited to walk and to simulate gait patterns representative of common neurological conditions: Cerebral Palsy, Multiple Sclerosis, and Cerebellar Ataxia. Generalized estimating equations were used to determine statistical differences between the measurement systems and between walking conditions. When comparing walking parameters between paired measures of the systems, significant differences were found for eight out of 18 descriptors: range of motion (ROM) of trunk and pelvis tilt, maximum knee flexion in loading response, knee position at toe-off, stride length, step time, cadence; and stance duration. When analyzing how ROBOGait can distinguish simulated pathological gait from physiological gait, a mean accuracy of 70.4%, a sensitivity of 49.3%, and a specificity of 74.4% were found when compared with the Xsens system. The most important gait abnormalities related to the clinical conditions were successfully detected by ROBOGait. The descriptors that best distinguished simulated pathological walking from normal walking in both systems were step width and stride length. This study underscores the promising potential of 3D cameras and encourages exploring their use in clinical gait analysis. © 2023 by the authors.
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Planning and Management of Fiber Optic Networks Based on a Geographic Information System: A Case in Ecuador
(2025)
Miguel Vargas-Bustamante
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Zapata, Mireya
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Jorge Álvarez-Tello
Lean management contributes to pursuit the quality, accessibility and efficiency of healthcare institutions. Its importance of the implementation of lean management in the healthcare sector using different strategies to improve services like: the supply chain in hospitals. In this review was used the model proposed by seuring and gold based on the analyze a sample of articles indexed in Scopus, web of science and also the simulation tool was used to in order to illustrate the approach. After implementation of lean thinking in different hospital in some countries improves the structure, process and outcome of care and management actions and management in healthcare promotes advantages in terms of quality, safety and efficiency of healthcare in hospitals
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AI-assisted neurocognitive assessment protocol for older adults with psychiatric disorders
(2025)
Diego D. Díaz-Guerra
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Marena de la C. Hernández-Lugo
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Yunier Broche-Pérez
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Carlos Ramos-Galarza
;
Ernesto Iglesias-Serrano
Introduction: Evaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificial intelligence (AI) with the depth of neuropsychological assessments is needed. Objectives: This paper presents a novel protocol for AI-assisted neurocognitive assessment aimed at addressing the cognitive, emotional, and functional dimensions of older adults with psychiatric disorders. It also explores potential compensatory mechanisms. Methodology: The proposed protocol incorporates a comprehensive, personalized approach to neurocognitive evaluation. It integrates a series of standardized and validated psychometric tests with individualized interpretation tailored to the patient’s specific conditions. The protocol utilizes AI to enhance diagnostic accuracy by analyzing data from these tests and supplementing observations made by researchers. Anticipated results: The AI-assisted protocol offers several advantages, including a thorough and customized evaluation of neurocognitive functions. It employs machine learning algorithms to analyze test results, generating an individualized neurocognitive profile that highlights patterns and trends useful for clinical decision-making. The integration of AI allows for a deeper understanding of the patient’s cognitive and emotional state, as well as potential compensatory strategies. Conclusions: By integrating AI with neuro-psychological evaluation, this protocol aims to significantly improve the quality of neurocognitive assessments. It provides a more precise and individualized analysis, which has the potential to enhance clinical decision-making and overall patient care for older adults with psychiatric disorders
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Analysis of Ergonomic Risks Based on Physical and Postural Characteristics in the Food Industry
(2026)
Buele, Jorge
Musculoskeletal disorders are one of the leading causes of occupational disability worldwide, affecting workers across various industries, particularly in the food industry. These disorders are caused by factors such as improper postures, repetitive movements, and manual handling of loads. Despite efforts to mitigate these risks, ergonomic assessment in many work environments remains insufficient, as it is often limited to posture observation and does not include precise anthropometric measurements. This study aims to integrate anthropometric measurements with the Rapid Entire Body Assessment method for a more accurate and personalized evaluation of ergonomic risks in the food industry. Through a quantitative approach, anthropometric measurements of workers in a food production plant were analyzed and the Rapid Entire Body Assessment method was applied to assess working postures. The data obtained were statistically analyzed to identify correlations between the physical characteristics of employees and musculoskeletal discomfort. The results showed a significant correlation between anthropometric dimensions, such as elbow height and functional reach, with discomfort in areas such as the elbow, knees, and lower back. These findings emphasize the need to adapt workstations to the physical characteristics of employees to prevent injuries. The integration of anthropometric measurements and the Rapid Entire Body Assessment method provides a more accurate tool for assessing ergonomic risks and designing personalized interventions that improve occupational health and productivity in the food industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Estimation of Unmodeled Dynamics: Nonlinear MPC and Adaptive Control Law With Momentum Observer Dynamic
(2024)
Bryan S. Guevara
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Luis F. Recalde
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Viviana Moya
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Varela Aldas, José
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Daniel C. Gandolfo
This article proposes an enhancement to estimate unmodeled dynamics within the simplified dynamic model of a quadcopter by integrating three key methodologies: Nonlinear Model Predictive Control (NMPC), a Momentum Observer Dynamics (MOD), and an adaptive control law. Termed as Adaptive NMPC with MOD, this integrated approach leverages NMPC, implemented using the CasADi framework, for real-time decision-making, while the momentum observer facilitates system state estimation and uncertainty mitigation. Simultaneously, the adaptive control law adjusts parameters to estimate errors in unmodeled dynamics. Through digital twin and Model in Loop (MiL) simulations, the effectiveness of this framework is demonstrated. Specifically, the study focuses on the simplified quadcopter model, acknowledging often overlooked inherent dynamics resulting from the simplification by not considering the nonlinearities induced by the drone's attitude angles. Addressing these unmodeled dynamics is critical, and the Adaptive NMPC with MOD method emerges as a robust solution, showcasing its potential across various scenarios.
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Artificial Intelligence and Tomorrow’s Education
(2021)
Cóndor-Herrera, Omar
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Arias Flores, Hugo Patricio
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Jadán Guerrero, Janio
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Ramos Galarza, Carlos
Nowadays, there is a rapid technological progress around the world that has enabled realities long ago unimaginable. We live in a technological era that represents new possibilities and challenges for society, and for the educational models in each country [1]. Research on smart education, which has forced the educational community to rethink on new ways of learning and teaching has been developed globally. Due to the advent of artificial intelligence (AI), the educational model for both, teachers and students will change. Nevertheless, to transform educational systems, it is necessary to update and train students, educators, and administrators effectively [2]. This research aims to describe the possible applications of AI in education from: 1) the automation of administrative tasks; 2) collection and analysis of information [3] to create smart content; 3) the implementation of virtual assistants in the teaching-learning process; 4) the potential delivery of lectures by humanoid robots with AI. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Scopus© Citations 3 38
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Low-Cost Non-Wearable Fall Detection System Implemented on a Single Board Computer for People in Need of Care
(2024)
Vanessa Vargas
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Pablo Ramos
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Edwin A. Orbe
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Zapata, Mireya
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Valencia-Aragón, Kevin
This work aims at proposing an affordable, non-wearable system to detect falls of people in need of care. The proposal uses artificial vision based on deep learning techniques implemented on a Raspberry Pi4 4GB RAM with a High-Definition IR-CUT camera. The CNN architecture classifies detected people into five classes: fallen, crouching, sitting, standing, and lying down. When a fall is detected, the system sends an alert notification to mobile devices through the Telegram instant messaging platform. The system was evaluated considering real daily indoor activities under different conditions: outfit, lightning, and distance from camera. Results show a good trade-off between performance and cost of the system. Obtained performance metrics are: precision of 96.4%, specificity of 96.6%, accuracy of 94.8%, and sensitivity of 93.1%. Regarding privacy concerns, even though this system uses a camera, the video is not recorded or monitored by anyone, and pictures are only sent in case of fall detection. This work can contribute to reducing the fatal consequences of falls in people in need of care by providing them with prompt attention. Such a low-cost solution would be desirable, particularly in developing countries with limited or no medical alert systems and few resources.
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Real-Time Digital Twin Architecture for Immersive Industrial Automation Training
(2026)
Jessica S. Ortiz
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Víctor H. Andaluz
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Carvajal, Christian
Industrial automation laboratories often face limitations related to restricted access to industrial equipment, safety constraints, and limited scalability for hands-on experimentation. To address these challenges, this work proposes a real-time multi-layer Digital Twin architecture integrating a physical Siemens S7-1500 PLC, an immersive Unity-based virtual environment, HMI supervision, and IoT-enabled remote monitoring within a unified communication framework. The architecture is structured into physical, digital, and integration layers, enabling modular scalability and bidirectional synchronization between the physical process and its virtual representation through Ethernet TCP/IP communication. System performance was evaluated using synchronization metrics including communication latency, jitter, deterministic timing deviation, and event synchronization accuracy. Experimental results demonstrated stable PLC–Digital Twin communication with average latencies below 15 ms and jitter below 0.5 ms, ensuring reliable real-time interaction during continuous operation. A comparative evaluation with engineering students also showed improved learning conditions, achieving high perceived usability (SUS = 86/100) and reduced cognitive workload (NASA-TLX = 34/100). These results confirm the effectiveness of the proposed architecture as a scalable platform for Industry 4.0 training environments.</jats:p>
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Development of a Convolutional Neural Network for Detection of Ovarian Cancer Based on Computed Tomography Images
(2024)
Gabriela Narvaez-Chunillo
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Ronny Ordoñez-Sanchez
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Lizbeth Ortiz-Vinueza
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Diego Almeida-Galárraga
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Fernando Villalba-Meneses
Ovarian cancer is one of the most frequent gynecologic malignancies in women, but it is often detected in late stage, leaving patients with little time to follow a successful therapy. Specialists have opted to use computer-aided diagnosis (CAD) for the detection of ovarian cancer through the analysis of computed tomography (CT) images, in which the professional examines the size, shape and different characteristics that enable a precise diagnosis in the ovary. This present project purposes a Convolutional Neural Network (CNN) which consist on four convolutional layers; including two pooling layer and two fully-connected layer. The cancerous ovaries images is selected from the Cancer Imaging Achive dataset for training and validation of the model. Moreover, the training of the CNN contain filters to ensure that all of the images are the same dimensions and pixel size. The testing results from the training of the images showed that the proposed model obtained a range of accuracy that goes from 90.0% to the best of the cases 98.85%. The variables obtained like the data of the pressure and loss of the training were compared with those of the validation, allowing for the determination of a successful CNN training.
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Centro de investigación en Mecatrónica y Sistemas Interactivos

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