Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios

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.</p

Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios

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.Biomechatronics & Human-Machine Contro

RoboGait: a robotic system for non-invasive gait analysis

[EN] The most common methods used in gait analysis laboratories are systems based on the use of markers and/or sensors positioned all over the patient s body while performing a walking test. These approaches usually require individual calibration, a long time to set up the patient, and, therefore, discomfort of the users. Besides, some of the methods can only be performed in specific small scenarios that need to be previously set-up with external sensors. The presented system, RoboGait, is designed to overcome these problems while maintaining a good performance in terms of quality of the measurements provided. RoboGait is a mobile robotic platform that moves in front of a patient that is walking. The system measures the configuration of the patient s body using an RGBD camera mounted on the top. Initial measurements provided by the camera are processed using an Artificial Neural Network that improves the estimated kinematic and spatio-temporal signals of the patient s movement. This paper shows the effectiveness of the system by comparing with a validated method that uses a Vicon® system. Then, the work shows the usefulness of RoboGait in a clinical environment by using it to classify healthy and pathological gaits. In this case, the results have been compared to a reference system based on inertial sensors called Xsens®. The results show a great potential for the use of RoboGait for clinicalpatient assessment and monitoring, and for pathology identification.[ES] 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. Adem´as, 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.Esta investigación ha recibido financiación del Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i y de I+D+i Orientada a los Retos de la Sociedad del Ministerio de Ciencia e Innovación de España, PID2020-118299RB.Álvarez, D.; Guffanti, D.; Brunete, A.; Hernando, M.; Gambao, E. (2023). RoboGait: sistema robótico no invasivo para el análisis de la marcha humana. Revista Iberoamericana de Automática e Informática industrial. 21(2):137-147. https://doi.org/10.4995/riai.2023.2006613714721

DG-CST (Disease Gene Conserved Sequence Tags), a database of human�mouse conserved elements associated to disease genes

The identification and study of evolutionarily conserved genomic sequences that surround disease-related genes is a valuable tool to gain insight into the functional role of these genes and to better elucidate the pathogenetic mechanisms of disease. We created the DG-CST (Disease Gene Conserved Sequence Tags) database for the identification and detailed annotation of human–mouse conserved genomic sequences that are localized within or in the vicinity of human disease-related genes. CSTs are defined as sequences that show at least 70% identity between human and mouse over a length of at least 100 bp. The database contains CST data relative to over 1088 genes responsible for monogenetic human genetic diseases or involved in the susceptibility to multifactorial/polygenic diseases. DG-CST is accessible via the internet at http://dgcst.ceinge.unina.it/ and may be searched using both simple and complex queries. A graphic browser allows direct visualization of the CSTs and related annotations within the context of the relative gene and its transcripts

DG-CST (Disease Gene Conserved Sequence Tags), a database of human–mouse conserved elements associated to disease genes

The identification and study of evolutionarily conserved genomic sequences that surround disease-related genes is a valuable tool to gain insight into the functional role of these genes and to better elucidate the pathogenetic mechanisms of disease. We created the DG-CST (Disease Gene Conserved Sequence Tags) database for the identification and detailed annotation of human–mouse conserved genomic sequences that are localized within or in the vicinity of human disease-related genes. CSTs are defined as sequences that show at least 70% identity between human and mouse over a length of at least 100 bp. The database contains CST data relative to over 1088 genes responsible for monogenetic human genetic diseases or involved in the susceptibility to multifactorial/polygenic diseases. DG-CST is accessible via the internet at http://dgcst.ceinge.unina.it/ and may be searched using both simple and complex queries. A graphic browser allows direct visualization of the CSTs and related annotations within the context of the relative gene and its transcripts

High Risk of Secondary Infections Following Thrombotic Complications in Patients With COVID-19

Background. This study’s primary aim was to evaluate the impact of thrombotic complications on the development of secondary infections. The secondary aim was to compare the etiology of secondary infections in patients with and without thrombotic complications. Methods. This was a cohort study (NCT04318366) of coronavirus disease 2019 (COVID-19) patients hospitalized at IRCCS San Raffaele Hospital between February 25 and June 30, 2020. Incidence rates (IRs) were calculated by univariable Poisson regression as the number of cases per 1000 person-days of follow-up (PDFU) with 95% confidence intervals. The cumulative incidence functions of secondary infections according to thrombotic complications were compared with Gray’s method accounting for competing risk of death. A multivariable Fine-Gray model was applied to assess factors associated with risk of secondary infections. Results. Overall, 109/904 patients had 176 secondary infections (IR, 10.0; 95% CI, 8.8–11.5; per 1000-PDFU). The IRs of secondary infections among patients with or without thrombotic complications were 15.0 (95% CI, 10.7–21.0) and 9.3 (95% CI, 7.9–11.0) per 1000-PDFU, respectively (P = .017). At multivariable analysis, thrombotic complications were associated with the development of secondary infections (subdistribution hazard ratio, 1.788; 95% CI, 1.018–3.140; P = .043). The etiology of secondary infections was similar in patients with and without thrombotic complications. Conclusions. In patients with COVID-19, thrombotic complications were associated with a high risk of secondary infections

A prospective examination of sex differences in posttraumatic autonomic functioning.

Background: Cross-sectional studies have found that individuals with posttraumatic stress disorder (PTSD) exhibit deficits in autonomic functioning. While PTSD rates are twice as high in women compared to men, sex differences in autonomic functioning are relatively unknown among trauma-exposed populations. The current study used a prospective design to examine sex differences in posttraumatic autonomic functioning. Methods: 192 participants were recruited from emergency departments following trauma exposure ( Results: 2-week systolic BP was significantly higher in men, while 2-week HR was significantly higher in women, and a sex by PTSD interaction suggested that women who developed PTSD demonstrated the highest HR levels. Two-week HF-HRV was significantly lower in women, and a sex by PTSD interaction suggested that women with PTSD demonstrated the lowest HF-HRV levels. Skin conductance response in the emergency department was associated with 2-week HR and HF-HRV only among women who developed PTSD. Conclusions: Our results indicate that there are notable sex differences in autonomic functioning among trauma-exposed individuals. Differences in sympathetic biomarkers (BP and HR) may have implications for cardiovascular disease risk given that sympathetic arousal is a mechanism implicated in this risk among PTSD populations. Future research examining differential pathways between PTSD and cardiovascular risk among men versus women is warranted

The DUNE far detector vertical drift technology. Technical design report

DUNE is an international experiment dedicated to addressing some of the questions at the forefront of particle physics and astrophysics, including the mystifying preponderance of matter over antimatter in the early universe. The dual-site experiment will employ an intense neutrino beam focused on a near and a far detector as it aims to determine the neutrino mass hierarchy and to make high-precision measurements of the PMNS matrix parameters, including the CP-violating phase. It will also stand ready to observe supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector implements liquid argon time-projection chamber (LArTPC) technology, and combines the many tens-of-kiloton fiducial mass necessary for rare event searches with the sub-centimeter spatial resolution required to image those events with high precision. The addition of a photon detection system enhances physics capabilities for all DUNE physics drivers and opens prospects for further physics explorations. Given its size, the far detector will be implemented as a set of modules, with LArTPC designs that differ from one another as newer technologies arise. In the vertical drift LArTPC design, a horizontal cathode bisects the detector, creating two stacked drift volumes in which ionization charges drift towards anodes at either the top or bottom. The anodes are composed of perforated PCB layers with conductive strips, enabling reconstruction in 3D. Light-trap-style photon detection modules are placed both on the cryostat's side walls and on the central cathode where they are optically powered. This Technical Design Report describes in detail the technical implementations of each subsystem of this LArTPC that, together with the other far detector modules and the near detector, will enable DUNE to achieve its physics goals

Design of a non-invasive cost-effective mobile robotic system for human gait analysis optimized by machine learning algorithms

This dissertation started from an original and clinically interesting question, namely, how can technology contribute to the objective assessment of the gait pattern of patients in clinical practice. In the search for an answer to this question, this thesis analyzes the viability of using a cost-effective and non-invasive mobile robotic platform for human gait analysis and establishes the basis for the design of a new prototype based on depth cameras and optimized by machine learning algorithms to perform the analysis of human gait in practical scenarios. This involves the development of the system and its validation for human gait analysis. Initially, has been assumed that the system must be non-invasive and cost-effective, but also the accuracy must be good enough to analyze human gait in a proper way. Following these guidelines, the core of this thesis has been the fusion of depth sensors with robotics to provide the platform with precision, usability, transportability, non-invasiveness and autonomy, which are the main requirements for the design of a gait analysis system. The initial step has been the exploration of three methods for using depth cameras in human gait analysis. Ordered according to their importance in the development of this thesis: the first method has proposed the use of one depth camera to analyze walking over a treadmill; the second one has proposed the use of several depth cameras in a multisensor configuration, with the cameras placed in a row; and the third one has proposed the integration of one depth camera on a mobile robot. The last two methods deal with overground gait analysis. Regarding the treadmill, two types have been used: motorized and non-motorized. The non-motorized treadmills had the disadvantage that they required great effort and had to be used tilted, which occluded the view of the sensor. Motorized treadmills were a better solution. Because they lay flat on the floor, the person does not need to grip the handle and require less effort (because they do not have to push with their feet to move the treadmill). However, they also showed some disadvantages, the most relevant of which was that they altered the gait pattern and the resultant pattern was different from that of an overground walking (as shown in Chapter 4, section 2). The aim of the second method was to extend the range of view of the depth camera using a multi-sensor configuration. This method presented some advantages. The use of two or more depth cameras allowed the capture of at least two gait cycles with each foot (the more cameras, the more gait cycles that can be detected). This also allowed the detection of gait pattern variables based on signal periodicity and frequency spectrum. However, this configuration had some disadvantages: several depth cameras were needed and these had to be calibrated before use; the recording was made only in a straight line and was limited to a few meters; finally, this system was difficult to move from one place to another. This conflicted with the philosophy of portability that was proposed for the system. Finally, the third method was the integration of a depth camera into a mobile robot. This method had several advantages: it was a cost-effective, non-invasive method with an unlimited detection range with the ability to adjust to the environment; the system did not alter the walking pattern; the robot could even be used outside a laboratory environment; and only one depth camera was needed. The design of this robotic system was performed in two stages. In the first stage a straight-line follower robot for human gait analysis was designed and validated. The idea of this robot was to follow the human from the front while walking in a straight line. 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) to compensate for sensor latency. During the validation procedure, the accuracy of the system to retrieve kinematic gait data was calculated with respect to the ground truth of a Vicon system. Following the experience acquired in the previous studies, certain shortcomings of the depth sensors in the analysis of human gait were detected. Therefore, it became necessary to work at a stage where the accuracy of the sensor is addressed. For this purpose, an extensive data collection stage was carried out together with the robot and a Vicon certified system. Then, using machine learning algorithms, supervised learning was applied by using the data from the Vicon system to train neural networks capable of improving the accuracy of the sensor in gait analysis. In this way the accuracy of the sensor improved significantly and most of the discrepancies between the biomechanical gait models applied by the robot and by the Vicon system disappeared. Continuing with the design of the robot, the second stage focused on adapting the platform for use in real environments. In this configuration, measurements were more natural because the tests were performed in real environments, in contrast to the use of dedicated laboratories. Using simultaneous localization and mapping (SLAM), control techniques, and path planning algorithms, this mobile robot was able to design flexible trajectories for gait experiments. During a gait experiment, the execution of control tasks in the robot were performed by a double controller: lane keeping and person following. The platform was tested in clinical environments with Multiple Sclerosis (MS) patients. Parallel to the construction of the robot, an interface for robot operation, gait data management and post-processing has been developed. This is an application for tablets and mobile devices that allows medical staff to operate the mobile robotic system accurately, without the need for training sessions or technical expertise. Finally, a comparison of the mobile robotic system and an inertial sensor system was developed. Normal and pathological gait patterns were analyzed both systems. The main differences between normal and pathological gait were identified based on joint kinematics and the main descriptors of gait. At the end, the performance of the systems to classify gait patterns was compared, allowing the mobile robotic system to be tested over a wider range of gait while being compared to a commercial system working under the same experimental conditions. To summarize, the main contributions that can be found in this thesis are: the scientific community has been provided with the main limitations and advantages of each configuration of gait analysis with depth sensors; the design, construction and validation of a mobile robotic system able to perform human gait analysis; control architecture designed for person following from the frontal part; improvement of the accuracy of depth sensors for human gait analysis through supervised learning from a Vicon system; the design of an application for tablets and mobile devices that allows medical staff to operate the robot; and the identification of the main differences in normal and pathological patterns on the basis of joint kinematics and the main descriptors of gait. ----------RESUMEN---------- Esta tesis parte de una pregunta original y de interés clínico, a saber, cómo puede contribuir la tecnología a la evaluación objetiva del patrón de la marcha de los pacientes en la práctica clínica. En la búsqueda de una respuesta a esta pregunta, esta tesis analiza la viabilidad de utilizar una plataforma robótica móvil rentable y no invasiva para el análisis de la marcha humana, y establece las bases para el diseño de un nuevo prototipo basado en cámaras de profundidad y optimizado mediante algoritmos de aprendizaje automático para realizar el análisis de la marcha humana en escenarios prácticos. Esto implica el desarrollo del sistema y su validación para el análisis de la marcha humana. Inicialmente, se ha asumido que el sistema debe ser no invasivo y rentable, pero también la precisión debe ser lo suficientemente buena como para analizar la marcha humana de forma adecuada. Siguiendo estas directrices, el núcleo de esta tesis ha sido la fusión de los sensores de profundidad con la robótica para dotar a la plataforma de precisión, usabilidad, transportabilidad, no invasividad y autonomía, que son los principales requisitos para el diseño de un sistema de análisis de la marcha. El paso inicial ha sido la exploración de tres métodos para el uso de cámaras de profundidad en el análisis de la marcha humana. Ordenados según su importancia en el desarrollo de esta tesis: el primer método ha propuesto el uso de una cámara de profundidad para analizar la marcha sobre una cinta de correr; el segundo ha propuesto el uso de varias cámaras de profundidad en una configuración multisensor, con las cámaras colocadas en fila; y el tercero ha propuesto la integración de una cámara de profundidad en un robot móvil. Los dos últimos métodos se ocupan del análisis de la marcha sobre el suelo. En cuanto a la cinta de correr, se han utilizado dos tipos: motorizada y no motorizada. Las cintas de correr no motorizadas tenían el inconveniente de que requerían un gran esfuerzo y debían utilizarse inclinadas, lo que ocluía la vista del sensor. Las cintas de correr motorizadas fueron una solución mejor. Como se colocan planas en el suelo, la persona no necesitó agarrarse el mango y por lo tanto requirió menos esfuerzo (porque el participante no tuvo que empujar con los pies para mover la cinta). Sin embargo, esta configuración también presentó algunas desventajas, la más relevante de las cuales era que alteraban el patrón de la marcha y el patrón resultante era diferente al de una marcha sobre el suelo (como se muestra en el capítulo 4, sección 2). El objetivo del segundo método fue ampliar el rango de visión de la cámara de profundidad mediante una configuración multisensor. Este método presentó algunas ventajas. El uso de dos o más cámaras de profundidad permitió capturar al menos dos ciclos de marcha con cada pie (cuantas más cámaras, más ciclos de marcha se pueden detectar). Esto también permitió la detección de variables del patrón de marcha basadas en la periodicidad de la señal y el espectro de frecuencia. Sin embargo, esta configuración tuvo algunas desventajas: se necesitaban varias cámaras de profundidad y éstas debían calibrarse antes de su uso; la grabación se realizaba sólo en línea recta y estaba limitada a unos pocos metros; por último, este sistema era difícil de trasladar de un lugar a otro. Esto iba en contra de la filosofía de portabilidad y no invasividad que fue propuesta para el sistema. Por último, el tercer método fue la integración de una cámara de profundidad en un robot móvil. Este método presentó varias ventajas: fue un método rentable y no invasivo con un rango de detección ilimitado con la posibilidad de ajustarse al entorno; el sistema no alteraba el patrón de marcha; el robot podía utilizarse incluso fuera de un entorno de laboratorio; y sólo se necesitaba una cámara de profundidad. El diseño de este sistema robótico se realizó en dos etapas. En la primera etapa se diseñó y validó un robot seguidor para el análisis de la marcha humana. La idea de este robot era seguir al humano de frente mientras caminaba en línea recta. Durante la etapa del diseño de control, se realizó la identificación del modelo y la configuración de la ley de control. El diseño de la ley de control requirió la integración de un compensador de adelanto y un Predictor de Smith filtrado (FSP) para compensar la latencia de los sensores. Durante el procedimiento de validación, se calculó la precisión del sistema para detectar los datos cinemáticos de la marcha con respecto a un sistema Vicon. Tras la experiencia adquirida en los estudios anteriores, se detectaron ciertas falencias de los sensores de profundidad en el análisis de la marcha humana. Por lo tanto, se hizo necesario trabajar en una etapa en la que abordáramos la precisión del sensor. Para ello, desarrollamos una etapa de recogida de datos de gran tamaño junto con el robot y un sistema certificado Vicon. A continuación, mediante algoritmos de aprendizaje de máquina, se aplicó un aprendizaje supervisado utilizando los datos del sistema Vicon para entrenar redes neuronales capaces de mejorar la precisión del sensor en el análisis de la marcha. De este modo, la precisión del sensor mejoró significativamente y la mayoría de las discrepancias entre los modelos biomecánicos de la marcha aplicados por el robot y por el sistema Vicon desaparecieron. Siguiendo con el diseño del robot, la segunda etapa se centró en adaptar la plataforma para su uso en entornos reales. En esta configuración, las mediciones fueron más naturales porque las pruebas se realizaban en entornos reales, en contraste con el uso de laboratorios dedicados. Utilizando la localization y el mapeo simultáneos (SLAM), técnicas de control y algoritmos de planificación de trayectorias, este robot móvil fue capaz de diseñar trayectorias flexibles para los experimentos de marcha. Durante un experimento de marcha, la ejecución de las tareas de control en el robot fue realizada por un doble controlador: de mantenimiento de carril y de seguimiento de la persona. La plataforma se probó en entornos clínicos con pacientes de esclerosis múltiple (EM). Paralelamente a la construcción del robot, se ha desarrollado una interfaz para el manejo del mismo, la gestión de los datos de la marcha y el posprocesamiento. Esta interfaz se trata de una aplicación para tabletas y dispositivos móviles que permite al personal médico manejar el sistema robótico móvil con precisión, sin necesidad de sesiones de formación ni conocimientos técnicos. Por último, se ha desarrollado una comparación entre el sistema robótico móvil y un sistema de sensores inerciales. Se analizaron patrones de marcha normales y patológicos utilizando el sistema robótico móvil y un sistema de sensores inerciales. Se identificaron las principales diferencias entre la marcha normal y la patológica sobre la base de la cinemática de las articulaciones y los principales descriptores de la marcha. Al final, se comparó el rendimiento de los sistemas para clasificar los patrones de la marcha, lo que permitió probar el sistema robótico móvil en un rango más amplio de la marcha y compararlo con un sistema comercial que trabaja en las mismas condiciones experimentales. En resumen, las principales aportaciones que se pueden encontrar en esta tesis son: se ha proporcionado a la comunidad científica las principales limitaciones y ventajas de cada configuración de análisis de la marcha con sensores de profundidad; el diseño, construcción y validación de un sistema robótico móvil capaz de realizar análisis de la marcha humana; la arquitectura de control diseñada para el seguimiento de personas desde la parte frontal; la mejora de la precisión de los sensores de profundidad para el análisis de la marcha humana mediante el aprendizaje supervisado de un sistema Vicon; el diseño de una aplicación para tabletas y dispositivos móviles que permita al personal médico manejar el robot; y la identificación de las principales diferencias entre patrones normales y patológicos a partir de la cinemática articular y los principales descriptores de la marcha

Artículo Científico.- Control remoto por voz del robot móvil Pioneer P3-DX

El presente artículo describe el diseño e implementación de un sistema de control remoto por voz para el robot móvil Pioneer P3-DX que posee el Departamento de Eléctrica y Electrónica de la ESPE. Como requisito previo para realizar el control de la plataforma robótica se han integrado dos entornos de programación distintos: MATLAB y ARIA. En el sistema de reconocimiento se utilizan herramientas de procesamiento de señales de manera que se elimine el ruido y se puedan extraer características fundamentales de la voz para que sean comparadas con cada una de las características almacenadas en una base de datos. La señal almacenada con menor medida de distancia a la grabada representa la de mayor similitud