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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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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].
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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
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Alberto Brunete
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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.
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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.
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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
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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.
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ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments
(MDPI AG, 2021-10-13)
GUFFANTI MARTINEZ, DIEGO ANDRES
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Alberto Brunete
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Miguel Hernando
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Javier Rueda
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Enrique Navarro
Mobile robotic platforms have made inroads in the rehabilitation area as gait assistance devices. They have rarely been used for human gait monitoring and analysis. The integration of mobile robots in this field offers the potential to develop multiple medical applications and achieve new discoveries. This study proposes the use of a mobile robotic platform based on depth cameras to perform the analysis of human gait in practical scenarios. The aim is to prove the validity of this robot and its applicability in clinical settings. The mechanical and software design of the system is presented, as well as the design of the controllers of the lane-keeping, person-following, and servoing systems. The accuracy of the system for the evaluation of joint kinematics and the main gait descriptors was validated by comparison with a Vicon-certified system. Some tests were performed in practical scenarios, where the effectiveness of the lane-keeping algorithm was evaluated. Clinical tests with patients with multiple sclerosis gave an initial impression of the applicability of the instrument in patients with abnormal walking patterns. The results demonstrate that the system can perform gait analysis with high accuracy. In the curved sections of the paths, the knee joint is affected by occlusion and the deviation of the person in the camera reference system. This issue was greatly improved by adjusting the servoing system and the following distance. The control strategy of this robot was specifically designed for the analysis of human gait from the frontal part of the participant, which allows one to capture the gait properly and represents one of the major contributions of this study in clinical practice.
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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
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Alberto Brunete
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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.
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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>
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Mechatronic Design of a Self-Contained Dexterous Robotic Hand for Gestural Communication
(Springer Science and Business Media LLC, 2023-01-13)
Miguel Hernando
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Carlos Morillo
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GUFFANTI MARTINEZ, DIEGO ANDRES
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Alberto Brunete
The hand is the most representative tool of the human body because of its complexity, dexterity, and precision. The high amount of both electronic and mechanical components results in a very difficult integration of all components inside the hand. The main objective of this work is to develop a fully integrated gestural robotic hand based on new design concepts that combine mechanics and electronics to provide a solution to the problem of limited space. ManoPla is a gestural hand, so its naturalness, low weight and full integration are the main design requirements. In this way, mechanisms that allow joint mobility have been developed using elastic components. The Color Gradient Method was applied to feed back the position of the joints. In addition, a novel thumb design with 4 degrees of freedom has been developed, including an advanced trapezium design. The Cutkosky grasp taxonomy was successfully reproduced to demonstrate the mobility of the hand proposed in this study. An impact test demonstrated the flexibility of the joints thanks to the SEA actuation approach. The main contributions of this paper are new design concepts to integrate mechanics and electronics inside the hand. In this way, ManoPla can easily be included as a gestural module of a humanoid robot for social interaction.
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RoboGait: sistema robótico no invasivo para el análisis de la marcha humana
(Universitat Politecnica de Valencia, 2023-10-31)
David Álvarez
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GUFFANTI MARTINEZ, DIEGO ANDRES
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Alberto Brunete
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Miguel Hernando
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Ernesto Gambao
Actualmente, los sistemas utilizados en laboratorios para analizar la marcha se basan en técnicas marcadores o sensores colocados sobre el cuerpo del paciente, lo que resulta en un proceso que requiere un tiempo largo de preparación y calibración, así como la incomodidad que causa a los pacientes tener dispositivos colocados por el cuerpo. Además, el espacio en el que se pueden realizar pruebas resulta muy limitado. En respuesta a estas problemáticas, se ha desarrollado el sistema robótico RoboGait. Consiste en un robot móvil capaz de navegar autónomamente delante del paciente. El robot incluye una cámara RGBD en su parte superior para captar el cuerpo humano. Este sistema no requiere marcadores adheridos al cuerpo del paciente ya que utiliza la información proporcionada por la cámara RGBD para analizar la marcha. El objetivo de este estudio es demostrar la validez de RoboGait y su aplicabilidad en entornos clínicos. Para conseguirlo, se ha optado por mejorar la estimación de señales cinemáticas y espacio-temporales de la marcha procesando las medidas de la cámara con redes neuronales artificiales (RNA) entrenadas usando datos obtenidos de un sistema Vicon certificado. Posteriormente, se ha medido el rendimiento del sistema en la clasificación de patrones normales y patológicos, utilizando como referencia un sistema basado en sensores inerciales Xsens. De este modo, se ha probado el sistema robótico móvil en un rango amplio de la marcha, al tiempo que se ha comparado con un sistema comercial en las mismas condiciones experimentales. Los resultados obtenidos demuestran que RoboGait puede realizar el análisis de la marcha con suficiente precisión,mostrando un gran potencial para su análisis clínico y la identificación de patologías.

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