GUFFANTI MARTÍNEZ, DIEGO ANDRES

Preferred name

Main Affiliation

Facultad de Ciencias de la Ingeniería e Industrias

GISER - Grupo de Investigación en Sistemas Embebidos y Robótica

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Google Scholar

ORCID

0000-0002-1244-291X

Scopus Author ID

57200385407

Now showing 1 - 10 of 21

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Optimizing Electrical Systems Stability: A Novel Lyapunov Framework for Harmonic Reduction and Power Quality Enhancement
(IEEE, 2024-08-05)
Wilson Pavón
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Michael Chamorro
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Ismael Michala
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GUFFANTI MARTINEZ, DIEGO ANDRES
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Mohan Kolhe
This study investigates power quality improvement through a Lyapunov-based harmonic reduction strategy utilizing an Active Hybrid Power Filter (SHAPF). Central to this approach is the application of state feedback to maintain system stability. A practical case study examining the influence of a Nonlinear Load (NLL) on an IEEE 13-bus distribution system validates the proposed methodology. Implemented and scrutinized via Matlab/Simulink, this research encompasses the detailed modeling of the SHAPF, its control strategies, and the analysis of harmonic distortion data. Such an in-depth simulation facilitates a rigorous evaluation of the method in conditions mirroring real-world scenarios, shedding light on its efficacy and applicability. Harnessing the principles of Lyapunov theory alongside advanced control techniques, the objective is to markedly diminish harmonic distortions in power systems, thereby significantly improving power quality and Electrical Compatibility (EC). This contribution highlights a commitment to enhancing the reliability and quality of modern power system engineering through innovative solutions.
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Human Gait Analysis Using Non-invasive Methods with a ROS-Based Mobile Robotic Platform
(Springer International Publishing, 2020-11-10)
GUFFANTI MARTINEZ, DIEGO ANDRES
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Alberto Brunete
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Miguel Hernando Gutierrez
Mobile robotic platforms for human gait analysis could open the gap to multiple medical applications and new discoveries. They could take several advantages over certified photogrammetric systems by making possible gait analysis without space limitations. In this document we present the design of a new ROS-based mobile robot platform for human gait analysis. All processes are ROS-based and Nuitrack SDK is used to develop the skeleton tracking application with a depth camera. During the procedure we described the design of the control law implemented for gait analysis. We developed a lead compensator by root locus method to increase the stability and speed response of the system. The error of measurement with respect to a certified photogrammetric system was considerably low during positioning task. Additional measurements were performed to verify the acquisition of gait parameters. These included spatio-temporal variables and range of movement (ROM) of knee and hip during joint excursions. Results showed that this mobile robotic platform represents a non-invasive alternative that could be improved for use in biomechanical human gait analysis.
Scopus© Citations 1
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Robotics‐driven gait analysis: Assessing Azure Kinect's performance in in‐lab versus in‐corridor environments
(Wiley, 2024-03-13)
GUFFANTI MARTINEZ, DIEGO ANDRES
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Alberto Brunete
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Miguel Hernando
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David Álvarez
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Ernesto Gambao
Abstract Gait analysis offers vital insights into human movement, aiding in the diagnosis, treatment, and rehabilitation of various conditions. Analyzing gait in corridors, rather than in lab, provides unique advantages for a more comprehensive understanding of human locomotion. However, limited dedicated technologies constrain gait data analysis in this context. In this study, a markerless gait analysis system using an Azure Kinect sensor mounted on a mobile robot is proposed and validated as a potential solution for gait analysis in corridors. Ten healthy participants (4 males and 6 females) underwent two tests. The first test (5 trials per participant) took place in the laboratory. Here, Azure Kinect performance was validated against a Vicon system, assessing eight gait signals and 22 gait parameters. The second test (2 trials per participant) was performed in the corridors over a 32‐m walking distance to compare this gait pattern with the one developed within the laboratory. The intrasession Intraclass Correlation Coefficient (ICC) reliability for in‐lab experiments was assessed by calculating the ICC between gait cycles captured in each session per participant. Notably, knee flexion/extension (ICC‐0.95), hip flexion/extension (ICC‐0.96), pelvis rotation (ICC‐0.88), and interankle distance (ICC‐0.98) demonstrated excellent reliability with high confidence. Similarly, hip adduction/abduction showed good reliability (ICC‐0.79), while trunk rotation exhibited moderate reliability (ICC‐0.72). In contrast, both trunk tilt (ICC‐0.24) and pelvis tilt (ICC‐0.41) consistently displayed lower reliability. This was observed for both the Vicon and the Azure systems, highlighting the intricate nature of capturing precise data for these specific signals in both systems. Validity outcomes indicated comparable error rates to literature standards ( knee flexion/extension, hip flexion/extension, and hip adduction/abduction), with 11 parameters having no significant differences from Vicon. Comparison of in‐lab and in‐corridor experiments show that individuals exhibit significantly longer stride time (1.10 s vs. 1.05 s), lower pelvis tilt ( vs. ), and lower minimum pelvis rotation ( vs. ) when walking in the laboratory. This study demonstrates promising outcomes in outdoor gait analysis with a robot‐mounted camera, revealing significant distinctions from controlled laboratory evaluations.
Scopus© Citations 5
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Supervised learning for improving the accuracy of robot-mounted 3D camera applied to human gait analysis
(Elsevier BV, 2024-02)
GUFFANTI MARTINEZ, DIEGO ANDRES
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Alberto Brunete
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Miguel Hernando
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David Álvarez
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Javier Rueda
Background and Objective: the use of 3D cameras for gait analysis has been highly questioned due to the low accuracy they have demonstrated in the past. The objective of the study presented in this paper is to improve the accuracy of the estimations made by robot-mounted 3D cameras in human gait analysis by applying a supervised learning stage. Methods: the 3D camera was mounted in a mobile robot to obtain a longer walking distance. This study shows an improvement in detection of kinematic gait signals and gait descriptors by post-processing the raw estimations of the camera using artificial neural networks trained with the data obtained from a certified Vicon system. To achieve this, 37 healthy participants were recruited and data of 207 gait sequences were collected using an Orbbec Astra 3D camera. There are two basic possible approaches for training and both have been studied in order to see which one achieves a better result. The artificial neural network can be trained either to obtain more accurate kinematic gait signals or to improve the gait descriptors obtained after initial processing. The former seeks to improve the waveforms of kinematic gait signals by reducing the error and increasing the correlation with respect to the Vicon system. The second is a more direct approach, focusing on training the artificial neural networks using gait descriptors directly. Results: the accuracy of the 3D camera to objectify human gait was measured before and after training. In both training approaches, a considerable improvement was observed. Kinematic gait signals showed lower errors and higher correlations with respect to the ground truth. The accuracy of the system to detect gait descriptors also showed a substantial improvement, mostly for kinematic descriptors rather than spatio-temporal. When comparing both training approaches, it was not possible to define which was the absolute best. Conclusions: supervised learning improves the accuracy of 3D cameras but the selection of the training approach will depend on the purpose of the study to be conducted. This study reveals the great potential of 3D cameras and encourages the research community to continue exploring their use in gait analysis.
Scopus© Citations 5
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Organic zinc sources in broiler production at high altitude under on-top supplementation or total or partial replacement: 1. Effects on performance and zinc excretion
(Elsevier BV, 2024-12)
R. Riboty
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J.L. Gaibor
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C.L. Ponce-de-Leon
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GUFFANTI MARTINEZ, DIEGO ANDRES
Feeding broilers with organic chemical sources of Zn has recently been recommended to improve performance, immune system, carcass yield, and reduce environmental contamination. However, its use under proposed supplementation strategies (i.e., total replacement, partial replacement, on-top) needs further investigation. This study assessed the effect of total replacement, partial replacement, and on-top supplementation strategies to feed organic Zn sources and the effect of two organic chemical forms on performance and Zn excretion in broilers at high altitudes. Twenty-two male Cobb 500-day-old chicks were placed in each of 54 floor pens and raised for up to 42 days under a three-phase feeding program and following the Ecuadorian highland production system. Pens were assigned to one of nine experimental treatments consisting of a basal diet supplemented with 0, 33, and 100 ppm of Zn from ZnSO4 and added or not 40 ppm Zn from Zn proteinate or Zn amino acid complex. A Completely Randomized Block Design was considered, being the block the replication. The Zn concentration of the basal diet was determined. Feed intake, BW, BW gain, feed conversion ratio, and European Production Efficiency Factor were assessed at 21 and 42 days of age, and the Zn excretion was estimated accordingly. Each supplementation strategy was compared with the standard practice (100 ppm Zn as ZnSO4) using contrasts and mixed models, and the interaction with the chemical form was assessed considering the P-values of the ANOVA and the multiple comparisons between the corresponding treatments. The effects of the organic Zn forms and their interactions with the dietary Zn level were assessed considering the responses were linear functions of the organic Zn source, the feed intake, the Zn intake, the Zn supplementation level, and the block, as corresponding. None of the strategies to supplement a Zn organic source, or the organic sources themselves, showed overall detectable effects on performance. However, interactions were observed between the supplementation strategy and the organic Zn source and between the organic source and the dietary Zn levels or the Zn intake. Under the on-top supplementation strategy, the Zn organic sources showed different feed conversion ratios at 21 days. In addition, totally or partially replacing the ZnSO4 with a Zn organic form reduced the Zn excretion. In conclusion, although no overall effect of the supplementation strategies was detected, the assessed organic Zn forms showed different effects on the feed conversion ratio at 21 days.
Scopus© Citations 4
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Comparison of Two System Identification Approaches for a Four-Wheel Differential Robot Based on Velocity Command Execution
(MDPI AG, 2025-06-05)
GUFFANTI MARTINEZ, DIEGO ANDRES
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Moisés Filiberto Mora Murillo
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Marco Alejandro Hinojosa
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Santiago Bustamante Sanchez
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Javier Oswaldo Obregón Gutiérrez
Precise modeling of differential drive robots is crucial for effective control and trajectory planning in autonomous systems. A comparative analysis of two modeling approaches for a four-wheel differential drive robot is presented in this paper. The first approach, named Motor-Based Model (MBM), identifies four transfer functions, one for each motor, while the second approach, named Simplified Model (SM), uses only two transfer functions, one for linear velocity and another for angular velocity. Both models were validated by comparing their predicted trajectories against real odometry data obtained from a SLAM system implemented on a differential-drive robot. This provided a practical assessment of each model’s accuracy and underscored the importance of model selection in control design and navigation tasks. The results showed that the Motor-Based Model (MBM) consistently outperformed the Simplified Model (SM) in terms of odometry accuracy, both in position and orientation. Across all trajectories, the average RMSE for position using MBM was 0.309 m, while the SM recorded a higher average RMSE of 0.414 m. Similarly, the maximum position error averaged 0.522 m for MBM and 0.710 m for SM, confirming that MBM is more accurate and consistent in position tracking. Regarding the results of orientation estimation, when averaged across all experiments, the MBM maintained a lower angular RMSE of 0.170 rad in contrast to SM, which achieves an RMSE of 0.239 rad. The maximum angular error was also higher for the MBM at 0.316 rad, compared to 0.447 rad for the SM. Moreover, the computational performance evaluation indicated that the SM consistently outperformed MBM, achieving a 30% reduction in simulation time and substantially lower memory usage. These results demonstrate the relationship between model complexity and accuracy and suggest that the motor-specific model is more appropriate for applications requiring precise mapping or localization, such as SLAM, while the simplified model may be suitable for simpler use cases with lower computational requirements, such as embedded systems with limited resources. This paper provides a practical evaluation of the accuracy and computational performance of two modeling approaches, highlighting the implications of model selection for the design of navigation tasks.
Scopus© Citations 5
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Editorial: Assistive and service robots for health and home applications (RH3 - Robot Helpers in Health and Home)
(Frontiers Media SA, 2024-10-29)
Paloma de la Puente
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Markus Vincze
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GUFFANTI MARTINEZ, DIEGO ANDRES
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Daniel Galan
In Choi et al. (2024), the main trends in assistive technologies for healthcare and home environments were analyzed. In that study, assistive technologies were classified into three major groups: physical aid or mobility devices, sensor and monitoring systems, and assistive robots. In an aging society, with people living longer and a lack of medical personnel, there is undoubtedly a growing interest in the development and commercialization of robotic systems that are able to provide support at healthcare facilities and home environments (Bajones et al., 2018; Keroglou et al., 2023; Silvera-Tawil, 2024). At healthcare facilities, robots improve the diagnosis and treatment of many different diseases (mental and physical), and they help professional staff be more efficient, with more time for patients. At home, they assist to prevent accidents, and they have the potential to perform different household tasks, keep the users active with cognitive and physical activities, and raise alarms if needed. Assistive robots designed for therapeutic purposes have proven useful in reducing agitation of elderly people and stress of caregivers (Kolstad et al., 2020).
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Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios
(MDPI AG, 2023-08-04)
GUFFANTI MARTINEZ, DIEGO ANDRES
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Daniel Lemus
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Heike Vallery
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Alberto Brunete
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Miguel Hernando
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.</jats:p>
Scopus© Citations 2
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Control Design and Validation of Gait Analysis with the Robogait Mobile Robotic Platform
(Springer Nature Switzerland, 2025-09-03)
GUFFANTI MARTÍNEZ, DIEGO ANDRES
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Alberto Brunete
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Miguel Hernando Gutierrez
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David Álvarez
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GUTIÉRREZ SUQUILLO, NELSON RAMIRO
The integration of mobile robotic platforms with depth sensors could led to a major advance in human gait analysis. However, the lack of dedicated technologies designed specifically for corridor-based gait analysis limits the availability of comprehensive tools to accurately and efficiently capture and analyze gait data in this specific context. In this study, control algorithms for person following and lane keeping of a mobile robotic platform named Robogait were applied and validated experimentally. The validity of using an Azure Kinect sensor for gait analysis was also examined using gait data collected from 10 participants and comparing its accuracy in gait signals and gait parameters with respect to a Vicon photogrammetric system. Results in controller design demonstrated a path following error of only 0.0446 m was measured on average, with a maximum deviation of 0.1420 m. The person tracking presented slight oscillations, however it did not affect the performance of the system in the gait analysis. An RMSE error of 12.68 was obtained for knee flex./ext., 5.54 for hip flex./ext., and just 0.06 m for the inter-ankle distance. Regarding gait descriptors analyzed, the Azure Kinect system provides reliable gait event measurements, though some discrepancies exist compared to Vicon. This study validates the use of the Azure Kinect sensor in gait analysis with mobile platforms. This offers a low-cost solution in real environments such as hospital corridors, contrary to in-lab gait analysis where the influence of equipment and the controlled environment could alter the gait pattern. The robot setup errors were comparable to static treadmill systems and similar to those of Vicon systems, which highlights its potential in clinical and rehabilitation applications.
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Multivariate System Identification of Differential Drive Robot: Comparison Between State-Space and LSTM-Based Models
(MDPI AG, 2025-09-18)
GUFFANTI MARTINEZ, DIEGO ANDRES
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PAVÓN VALLEJOS, WILSON DAVID
Modeling mobile robots is crucial to odometry estimation, control design, and navigation. Classical state-space models (SSMs) have traditionally been used for system identification, while recent advances in deep learning, such as Long Short-Term Memory (LSTM) networks, capture complex nonlinear dependencies. However, few direct comparisons exist between these paradigms. This paper compares two multivariate modeling approaches for a differential drive robot: a classical SSM and an LSTM-based recurrent neural network. Both models predict the robot’s linear (v) and angular (ω) velocities using experimental data from a five-minute navigation sequence. Performance is evaluated in terms of prediction accuracy, odometry estimation, and computational efficiency, with ground-truth odometry obtained via a SLAM-based method in ROS2. Each model was tuned for fair comparison: order selection for the SSM and hyperparameter search for the LSTM. Results show that the best SSM is a second-order model, while the LSTM used seven layers, 30 neurons, and 20-sample sliding windows. The LSTM achieved a FIT of 93.10% for v and 90.95% for ω, with an odometry RMSE of 1.09 m and 0.23 rad, whereas the SSM outperformed it with FIT values of 94.70% and 91.71% and lower RMSE (0.85 m, 0.17 rad). The SSM was also more resource-efficient (0.00257 ms and 1.03 bytes per step) compared to the LSTM (0.0342 ms and 20.49 bytes). The results suggest that SSMs remain a strong option for accurate odometry with low computational demand while encouraging the exploration of hybrid models to improve robustness in complex environments. At the same time, LSTM models demonstrated flexibility through hyperparameter tuning, highlighting their potential for further accuracy improvements with refined configurations.
Scopus© Citations 2

GUFFANTI MARTÍNEZ, DIEGO ANDRES

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