GUFFANTI MARTÍNEZ, DIEGO ANDRES

Preferred name
GUFFANTI MARTÍNEZ, DIEGO ANDRES
Main Affiliation
GISER - Grupo de Investigación en Sistemas Embebidos y Robótica
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ORCID
0000-0002-1244-291X
Scopus Author ID
57200385407

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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  
    ;
    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
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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
    ;
    Markus Vincze
    ;
    GUFFANTI MARTINEZ, DIEGO ANDRES  
    ;
    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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    Supervised learning for improving the accuracy of robot-mounted 3D camera applied to human gait analysis
    (Elsevier BV, 2024-02)
    GUFFANTI MARTINEZ, DIEGO ANDRES  
    ;
    Alberto Brunete
    ;
    Miguel Hernando
    ;
    David Álvarez
    ;
    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 6
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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  
    ;
    Alberto Brunete
    ;
    Miguel Hernando
    ;
    David Álvarez
    ;
    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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    Unmanned Aerial Vehicle Position Tracking Using Nonlinear Autoregressive Exogenous Networks Learned from Proportional-Derivative Model-Based Guidance
    (MDPI AG, 2025-07-24)
    PAVÓN VALLEJOS, WILSON DAVID  
    ;
    Jorge Chavez
    ;
    Ama Baduba Asiedu-Asante
    ;
    GUFFANTI MARTINEZ, DIEGO ANDRES  
    The growing demand for agile and reliable Unmanned Aerial Vehicles (UAVs) has spurred the advancement of advanced control strategies capable of ensuring stability and precision under nonlinear and uncertain flight conditions. This work addresses the challenge of accurately tracking UAV position by proposing a neural-network-based approach designed to replicate the behavior of classical control systems. A complete nonlinear model of the quadcopter was derived and linearized around a hovering point to design a traditional proportional derivative (PD) controller, which served as a baseline for training a nonlinear autoregressive exogenous (NARX) artificial neural network. The NARX model, selected for its feedback structure and ability to capture temporal dynamics, was trained to emulate the control signals of the PD controller under varied reference trajectories, including step, sinusoidal, and triangular inputs. The trained networks demonstrated performance comparable to the PD controller, particularly in the vertical axis, where the NARX model achieved a minimal Mean Squared Error (MSE) of 7.78×10−5 and an R2 value of 0.9852. These results confirm that the NARX neural network, trained via supervised learning to emulate a PD controller, can replicate and even improve classical control strategies in nonlinear scenarios, thereby enhancing robustness against dynamic changes and modeling uncertainties. This research contributes a scalable approach for integrating neural models into UAV control systems, offering a promising path toward adaptive and autonomous flight control architectures that maintain stability and accuracy in complex environments.
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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
    ;
    J.L. Gaibor
    ;
    C.L. Ponce-de-Leon
    ;
    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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    Human Gait Analysis Using Non-invasive Methods with a ROS-Based Mobile Robotic Platform
    (Springer International Publishing, 2020-11-10)
    GUFFANTI MARTINEZ, DIEGO ANDRES  
    ;
    Alberto Brunete
    ;
    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 2
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    Continuous Speech Recognition and Identification of the Speaker System
    (Springer International Publishing, 2018)
    GUFFANTI MARTINEZ, DIEGO ANDRES  
    ;
    Danilo Martínez
    ;
    José Paladines
    ;
    Andrea Sarmiento
    Currently speech recognition and speaker identification based on a biometric parameter such as voice have been treated as two different worlds and in the market there are no integrated applications of these systems. The design of a system could mean a great contribution to the development of personalized commands, in the area of home automation and robotics, thanks to the availability of the message and the identification of the speaker. Therefore, the development of an integrated biometric voice system is proposed, based on a single voice sample for the identification of the speaker and the message. We use GOOGLE SPEECH API, as a voice text translation tool, and Mel Frequency Cepstral Coefficients or MFCCs extracted from voice signal to identify speakers voice. Functional tests were carried with 50 randomly users, in the end of the study results show 96.4% efficiency in identification, demonstrating efficiency using MFCCs in speaker’s automatic recognition and verifying the use of GOOGLE SPEECH API as a fast, accurate and robust translation tool.
    Scopus© Citations 1
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    Technological Advancements in Human Navigation for the Visually Impaired: A Systematic Review
    (MDPI AG, 2025-04-01)
    Edgar Casanova
    ;
    GUFFANTI MARTINEZ, DIEGO ANDRES  
    ;
    HIDALGO AGUILERA, LUIS ARMANDO  
    Visually impaired people face significant obstacles when navigating complex environments. However, recent technological advances have greatly improved the functionality of navigation systems tailored to their needs. The objective of this research is to evaluate the effectiveness and functionality these navigation systems through a comparative analysis of recent technologies. For this purpose, the PRISMA 2020 methodology was used to perform a systematic literature review. After identification and screening, 58 articles published between 2019 and 2024 were selected from three academic databases: Dimensions (26 articles), Web of Science (18 articles), and Scopus (14 articles). Bibliometric analysis demonstrated a growing interest of the research community in the topic, with an average of 4.552 citations per published article. Even with the technological advances that have occurred in recent times, there is still a significant gap in the support systems for people with blindness due to the lack of digital accessibility and the scarcity of adapted support systems. This situation limits the autonomy and inclusion of people with blindness, so the need to continue developing technological and social solutions to ensure equal opportunities and full participation in society is evident. This study emphasizes the great advances with the integration of sensors such as high-precision GPS, ultrasonic sensors, Bluetooth, and various assistance apps for object recognition, obstacle detection, and trajectory generation, as well as haptic systems, which provide tactile information through wearables or actuators and improve spatial awareness. Current navigation algorithms were also identified in the review with methods including obstacle detection, path planning, and trajectory prediction, applied to technologies such as ultrasonic sensors, RGB-D cameras, and LiDAR for indoor navigation, as well as stereo cameras and GPS for outdoor navigation. It was also found that AI systems employ deep learning and neural networks to optimize both navigation accuracy and energy efficiency. Finally, analysis revealed that 79% of the 58 reviewed articles included experimental validation, 87% of which were on haptic systems and 40% on smartphones. These results underscore the importance of experimentation in the development of technologies for the mobility of people with visual impairment.
    Scopus© Citations 30