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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.
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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
;
Alberto Brunete
;
Miguel Hernando
;
Javier Rueda
;
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
;
Alberto Brunete
;
Miguel Hernando
;
Javier Rueda
;
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
;
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.
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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
;
Daniel Lemus
;
Heike Vallery
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Alberto Brunete
;
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
;
Carlos Morillo
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GUFFANTI MARTINEZ, DIEGO ANDRES
;
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
;
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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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.
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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.

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