GUFFANTI MARTÍNEZ, DIEGO ANDRES

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Facultad de Ciencias de la Ingeniería e Industrias

GISER - Grupo de Investigación en Sistemas Embebidos y Robótica

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ORCID

0000-0002-1244-291X

Scopus Author ID

57200385407

Now showing 1 - 10 of 22

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A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis
(MDPI AG, 2025-01-16)
Xiaofeng Han
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GUFFANTI MARTINEZ, DIEGO ANDRES
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Alberto Brunete
Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations. The relevant papers were subjected to analysis using the PRISMA method, and 72 articles that met the criteria for this research project were identified. A detailing of the most commonly used visual sensor systems, machine learning algorithms, human gait analysis parameters, optimal camera placement, and gait parameter extraction methods is presented in the analysis. The findings of this research indicate that non-invasive depth cameras are gaining increasing popularity within this field. Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. This review seeks to establish the foundations for future innovations that will facilitate the development of more effective, versatile, and user-friendly gait analysis tools, with the potential to significantly enhance human mobility, health, and overall quality of life. This work was supported by [GOBIERNO DE ESPANA/PID2023-150967OB-I00].
Scopus© Citations 48
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Non-Invasive Multi-Camera Gait Analysis System and its Application to Gender Classification
(Institute of Electrical and Electronics Engineers (IEEE), 2020)
GUFFANTI MARTINEZ, DIEGO ANDRES
;
Alberto Brunete
;
Miguel Hernando
Objective: Most studies have conducted human gait analysis using expensive and invasive photogrammetric systems. The objective of this study was to demonstrate that non-invasive and cost-effective systems based on depth cameras may be able to retrieve relevant features of human gait patterns. We aimed to prove this by solving the problem of gait classification by gender. Methods: 81 participants (40 female and 41 male) walked at a self-selected speed across a 4.8-meter walkway. Gait data was recorded using multiple depth sensors. Analysis in time domain included joint excursions by gait phases, range of movement (ROM), central tendency and dispersion measures, spatial variables, and center of mass (COM) position. The spectral analysis included principal frequency, magnitude, and phase shift during walking. Only features with significant differences by gender were used to train a support vector machine (SVM) classifier. Results: A total of 108 features presented significant differences by gender (p<; 0.05). On this basis, the accuracy of the chosen model was 96.7%. Trunk rotation, trunk sway, knee abduction/adduction, and pelvic obliquity were the most differentiated between the groups. The COM position shown a significant difference by gender (p=0.0065) with 51.7% and 51.0% for men and women respectively. Women proved to have significantly shorter normalized step width than men (p=0.0472). Conclusion: The proposed method was able to retrieve most of human gait features correctly, including differences in gait pattern by gender. Significance: Depth cameras represent a cost-effective system that could be used for a deeper biomechanical human gait analysis.
Scopus© Citations 25
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The Accuracy of the Microsoft Kinect V2 Sensor for Human Gait Analysis. A Different Approach for Comparison with the Ground Truth
(MDPI AG, 2020-08-07)
GUFFANTI MARTINEZ, DIEGO ANDRES
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Alberto Brunete
;
Miguel Hernando
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Javier Rueda
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Enrique Navarro Cabello
Several studies have examined the accuracy of the Kinect V2 sensor during gait analysis. Usually the data retrieved by the Kinect V2 sensor are compared with the ground truth of certified systems using a Euclidean comparison. Due to the Kinect V2 sensor latency, the application of a uniform temporal alignment is not adequate to compare the signals. On that basis, the purpose of this study was to explore the abilities of the dynamic time warping (DTW) algorithm to compensate for sensor latency (3 samples or 90 ms) and develop a proper accuracy estimation. During the experimental stage, six iterations were performed using the a dual Kinect V2 system. The walking tests were developed at a self-selected speed. The sensor accuracy for Euclidean matching was consistent with that reported in previous studies. After latency compensation, the sensor accuracy demonstrated considerably lower error rates for all joints. This demonstrated that the accuracy was underestimated due to the use of inappropriate comparison techniques. On the contrary, DTW is a potential method that compensates for the sensor latency, and works sufficiently in comparison with certified systems.
Scopus© Citations 36
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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
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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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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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Technological Advancements in Human Navigation for the Visually Impaired: A Systematic Review
(MDPI AG, 2025-04-01)
Edgar Casanova
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GUFFANTI MARTINEZ, DIEGO ANDRES
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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 28
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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
;
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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Development and validation of a ROS-based mobile robotic platform for human gait analysis applications
(Elsevier BV, 2021-11)
GUFFANTI MARTINEZ, DIEGO ANDRES
;
Alberto Brunete
;
Miguel Hernando
Background: The integration of mobile robotic platforms in human gait analysis offers the potential to develop multiple medical applications and achieve new discoveries. The aim of this paper is to present a first design and validation of a ROS-based mobile robotic platform for human gait analysis. Methods: During the design stage, the model identification and the configuration of the control law were performed. The design of the control law required the integration of a lead compensator and a Filtered Smith Predictor (FSP). During the validation procedure, the accuracy of the system to retrieve kinematic gait data and the main descriptors of gait disorders was calculated with respect to the ground truth of a Vicon system. For this purpose, one hundred gait recordings were processed thanks to the collaboration of twenty participants. The participants walked in a one-way straight line gait. Results: Results showed high correlation and low error rates mainly in joint excursions from sagittal and transverse planes. Conclusion: This gait analysis system demonstrated several advantages compared with the current approaches. The use of a mobile robotic platform allowed gait analysis in long tracking ranges and without space limitations. Furthermore, the design of a suitable control law allowed a smooth tracking of the person. This led to optimal results when assessing joint excursions. Significance: This system represents a cost-effective and non-invasive alternative that could be used for human gait analysis applications.
Scopus© Citations 19

GUFFANTI MARTÍNEZ, DIEGO ANDRES

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