2D-Tasks for Cognitive Rehabilitation

Neuropsychological Rehabilitation is a complex clinic process which tries to restore or compensate cognitive and behavioral disorders in people suffering from a central nervous system injury. Information and Communication Technologies (ICTs) in Biomedical Engineering play an essential role in this field, allowing improvement and expansion of present rehabilitation programs. This paper presents a set of cognitive rehabilitation 2D-Tasks for patients with Acquired Brain Injury (ABI). These tasks allow a high degree of personalization and individualization in therapies, based on the opportunities offered by new technologies.

PREVIRNEC A new platform for cognitive tele-rehabilitation

Acquired Brain Injury (ABI), either caused by vascular or traumatic nature, is one of the most important causes for neurological disabilities. People who suffer ABI see how their quality of life decreases, due to the affection of one or some of the cognitive functions (memory, attention, language or executive functions). The traditional cognitive rehabilitation protocols are too expensive, so every help carried out in this area is justified. PREVIRNEC is a new platform for cognitive tele-rehabilitation that allows the neuropsychologist to schedule rehabilitation sessions consisted of specifically designed tasks, plus offering an additional way of communication between neuropsychologists and patients. Besides, the platform offers a knowledge management module that allows the optimization of the cognitive rehabilitation to this kind of patients.

Metodologías de modelado, monitorización y asistencia robótica en neurorrehabilitación funcional de extremidad superior

La Organización Mundial de la Salud (OMS) prevé que para el año 2020, el Daño Cerebral Adquirido (DCA) estará entre las 10 causas más comunes de discapacidad. Estas lesiones, dadas sus consecuencias físicas, sensoriales, cognitivas, emocionales y socioeconómicas, cambian dramáticamente la vida de los pacientes y sus familias. Las nuevas técnicas de intervención precoz y el desarrollo de la medicina intensiva en la atención al DCA han mejorado notablemente la probabilidad de supervivencia. Sin embargo, hoy por hoy, las lesiones cerebrales no tienen ningún tratamiento quirúrgico que tenga por objetivo restablecer la funcionalidad perdida, sino que las terapias rehabilitadoras se dirigen hacia la compensación de los déficits producidos. Uno de los objetivos principales de la neurorrehabilitación es, por tanto, dotar al paciente de la capacidad necesaria para ejecutar las Actividades de Vida Diaria (AVDs) necesarias para desarrollar una vida independiente, siendo fundamentales aquellas en las que la Extremidad Superior (ES) está directamente implicada, dada su gran importancia a la hora de la manipulación de objetos. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma centrado en ofrecer una práctica personalizada, monitorizada y ubicua con una valoración continua de la eficacia y de la eficiencia de los procedimientos y con capacidad de generar conocimientos que impulsen la ruptura del paradigma de actual. Los nuevos objetivos consistirán en minimizar el impacto de las enfermedades que afectan a la capacidad funcional de las personas, disminuir el tiempo de incapacidad y permitir una gestión más eficiente de los recursos. Estos objetivos clínicos, de gran impacto socio-económico, sólo pueden alcanzarse desde una apuesta decidida en nuevas tecnologías, metodologías y algoritmos capaces de ocasionar la ruptura tecnológica necesaria que permita superar las barreras que hasta el momento han impedido la penetración tecnológica en el campo de la rehabilitación de manera universal. De esta forma, los trabajos y resultados alcanzados en la Tesis son los siguientes: 1. Modelado de AVDs: como paso previo a la incorporación de ayudas tecnológicas al proceso rehabilitador, se hace necesaria una primera fase de modelado y formalización del conocimiento asociado a la ejecución de las actividades que se realizan como parte de la terapia. En particular, las tareas más complejas y a su vez con mayor repercusión terapéutica son las AVDs, cuya formalización permitirá disponer de modelos de movimiento sanos que actuarán de referencia para futuros desarrollos tecnológicos dirigidos a personas con DCA. Siguiendo una metodología basada en diagramas de estados UML se han modelado las AVDs 'servir agua de una jarra' y 'coger un botella' 2. Monitorización ubícua del movimiento de la ES: se ha diseñado, desarrollado y validado un sistema de adquisición de movimiento basado en tecnología inercial que mejora las limitaciones de los dispositivos comerciales actuales (coste muy elevado e incapacidad para trabajar en entornos no controlados); los altos coeficientes de correlación y los bajos niveles de error obtenidos en los corregistros llevados a cabo con el sistema comercial BTS SMART-D demuestran la alta precisión del sistema. También se ha realizado un trabajo de investigación exploratorio de un sistema de captura de movimiento de coste muy reducido basado en visión estereoscópica, habiéndose detectado los puntos clave donde se hace necesario incidir desde un punto de vista tecnológico para su incorporación en un entorno real 3. Resolución del Problema Cinemático Inverso (PCI): se ha diseñado, desarrollado y validado una solución al PCI cuando el manipulador se corresponde con una ES humana estudiándose 2 posibles alternativas, una basada en la utilización de un Perceptrón Multicapa (PMC) y otra basada en sistemas Artificial Neuro-Fuzzy Inference Systems (ANFIS). La validación, llevada a cabo utilizando información relativa a los modelos disponibles de AVDs, indica que una solución basada en un PMC con 3 neuronas en la capa de entrada, una capa oculta también de 3 neuronas y una capa de salida con tantas neuronas como Grados de Libertad (GdLs) tenga el modelo de la ES, proporciona resultados, tanto de precisión como de tiempo de cálculo, que la hacen idónea para trabajar en sistemas con requisitos de tiempo real 4. Control inteligente assisted-as-needed: se ha diseñado, desarrollado y validado un algoritmo de control assisted-as-needed para una ortesis robótica con capacidades de actuación anticipatoria de la que existe un prototipo implementado en la actualidad. Los resultados obtenidos demuestran cómo el sistema es capaz de adaptarse al perfil disfuncional del paciente activando la ayuda en instantes anteriores a la ocurrencia de movimientos incorrectos. Esta estrategia implica un aumento en la participación del paciente y, por tanto, en su actividad muscular, fomentándose los procesos la plasticidad cerebral responsables del reaprendizaje o readaptación motora 5. Simuladores robóticos para planificación: se propone la utilización de un simulador robótico assisted-as-needed como herramienta de planificación de sesiones de rehabilitación personalizadas y con un objetivo clínico marcado en las que interviene una ortesis robotizada. Los resultados obtenidos evidencian como, tras la ejecución de ciertos algoritmos sencillos, es posible seleccionar automáticamente una configuración para el algoritmo de control assisted-as-needed que consigue que la ortesis se adapte a los criterios establecidos desde un punto de vista clínico en función del paciente estudiado. Estos resultados invitan a profundizar en el desarrollo de algoritmos más avanzados de selección de parámetros a partir de baterías de simulaciones Estos trabajos han servido para corroborar las hipótesis de investigación planteadas al inicio de la misma, permitiendo, asimismo, la apertura de nuevas líneas de investigación. Summary The World Health Organization (WHO) predicts that by the year 2020, Acquired Brain Injury (ABI) will be among the ten most common ailments. These injuries dramatically change the life of the patients and their families due to their physical, sensory, cognitive, emotional and socio-economic consequences. New techniques of early intervention and the development of intensive ABI care have noticeably improved the survival rate. However, in spite of these advances, brain injuries still have no surgical or pharmacological treatment to re-establish the lost functions. Neurorehabilitation therapies address this problem by restoring, minimizing or compensating the functional alterations in a person disabled because of a nervous system injury. One of the main objectives of Neurorehabilitation is to provide patients with the capacity to perform specific Activities of the Daily Life (ADL) required for an independent life, especially those in which the Upper Limb (UL) is directly involved due to its great importance in manipulating objects within the patients' environment. The incorporation of new technological aids to the neurorehabilitation process tries to reach a new paradigm focused on offering a personalized, monitored and ubiquitous practise with continuous assessment of both the efficacy and the efficiency of the procedures and with the capacity of generating new knowledge. New targets will be to minimize the impact of the sicknesses affecting the functional capabilitiies of the subjects, to decrease the time of the physical handicap and to allow a more efficient resources handling. These targets, of a great socio-economic impact, can only be achieved by means of new technologies and algorithms able to provoke the technological break needed to beat the barriers that are stopping the universal penetration of the technology in the field of rehabilitation. In this way, this PhD Thesis has achieved the following results: 1. ADL Modeling: as a previous step to the incorporation of technological aids to the neurorehabilitation process, it is necessary a first modelling and formalization phase of the knowledge associated to the execution of the activities that are performed as a part of the therapy. In particular, the most complex and therapeutically relevant tasks are the ADLs, whose formalization will produce healthy motion models to be used as a reference for future technological developments. Following a methodology based on UML state-chart diagrams, the ADLs 'serving water from a jar' and 'picking up a bottle' have been modelled 2. Ubiquitous monitoring of the UL movement: it has been designed, developed and validated a motion acquisition system based on inertial technology that improves the limitations of the current devices (high monetary cost and inability of working within uncontrolled environments); the high correlation coefficients and the low error levels obtained throughout several co-registration sessions with the commercial sys- tem BTS SMART-D show the high precision of the system. Besides an exploration of a very low cost stereoscopic vision-based motion capture system has been carried out and the key points where it is necessary to insist from a technological point of view have been detected 3. Inverse Kinematics (IK) problem solving: a solution to the IK problem has been proposed for a manipulator that corresponds to a human UL. This solution has been faced by means of two different alternatives, one based on a Mulilayer Perceptron (MLP) and another based on Artificial Neuro-Fuzzy Inference Systems (ANFIS). The validation of these solutions, carried out using the information regarding the previously generated motion models, indicate that a MLP-based solution, with an architecture consisting in 3 neurons in the input layer, one hidden layer of 3 neurons and an output layer with as many neurons as the number of Degrees of Freedom (DoFs) that the UL model has, is the one that provides the best results both in terms of precission and in terms of processing time, making in idoneous to be integrated within a system with real time restrictions 4. Assisted-as-needed intelligent control: an assisted-as-needed control algorithm with anticipatory actuation capabilities has been designed, developed and validated for a robotic orthosis of which there is an already implemented prototype. Obtained results demonstrate that the control system is able to adapt to the dysfunctional profile of the patient by triggering the assistance right before an incorrect movement is going to take place. This strategy implies an increase in the participation of the patients and in his or her muscle activity, encouraging the neural plasticity processes in charge of the motor learning 5. Planification with a robotic simulator: in this work a robotic simulator is proposed as a planification tool for personalized rehabilitation sessions under a certain clinical criterium. Obtained results indicate that, after the execution of simple parameter selection algorithms, it is possible to automatically choose a specific configuration that makes the assisted-as-needed control algorithm to adapt both to the clinical criteria and to the patient. These results invite researchers to work in the development of more complex parameter selection algorithms departing from simulation batteries Obtained results have been useful to corroborate the hypotheses set out at the beginning of this PhD Thesis. Besides, they have allowed the creation of new research lines in all the studied application fields.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Dysfunctional profile for patients in physical neurorehabilitation of upper limb

This paper proposes a first approach to Objective Motor Assessment (OMA) methodology. Also, it introduces the Dysfunctional profile (DP) concept. DP consists of a data matrix characterizing the Upper Limb (UL) physical alterations of a patient with Acquired Brain Injury (ABI) during the rehabilitation process. This research is based on the comparison methology of UL movement between subjects with ABI and healthy subjects as part of OMA. The purpose of this comparison is to classify subjects according to their motor control and subsequently issue a functional assessment of the movement. For this purpose Artificial Neural Networks (ANN) have been used to classify patients. Different network structures are tested. The obtained classification accuracy was 95.65%. This result allows the use of ANNs as a viable option for dysfunctional assessment. This work can be considered a pilot study for further research to corroborate these results.

A graphical tool for designing interactive video cognitive rehabilitation therapies

Acquired Brain Injury (ABI) has become one of the most common causes of neurological disability in developed countries. Cognitive disorders result in a loss of independence and therefore patients? quality of life. Cognitive rehabilitation aims to promote patients? skills to achieve their highest degree of personal autonomy. New technologies such as interactive video, whereby real situations of daily living are reproduced within a controlled virtual environment, enable the design of personalized therapies with a high level of generalization and a great ecological validity. This paper presents a graphical tool that allows neuropsychologists to design, modify, and configure interactive video therapeutic activities, through the combination of graphic and natural language. The tool has been validated creating several Activities of Daily Living and a preliminary usability evaluation has been performed showing a good clinical acceptance in the definition of complex interactive video therapies for cognitive rehabilitation.

Dysfunctional 3D model based on structural and neuropsychological information

Acquired brain injury (ABI) 1-2 refers to any brain damage occurring after birth. It usually causes certain damage to portions of the brain. ABI may result in a significant impairment of an individuals physical, cognitive and/or psychosocial functioning. The main causes are traumatic brain injury (TBI), cerebrovascular accident (CVA) and brain tumors. The main consequence of ABI is a dramatic change in the individuals daily life. This change involves a disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges in neurorehabilitation is to obtain a dysfunctional profile of each patient in order to personalize the treatment. This paper proposes a system to generate a patient s dysfunctional profile by integrating theoretical, structural and neuropsychological information on a 3D brain imaging-based model. The main goal of this dysfunctional profile is to help therapists design the most suitable treatment for each patient. At the same time, the results obtained are a source of clinical evidence to improve the accuracy and quality of our rehabilitation system. Figure 1 shows the diagram of the system. This system is composed of four main modules: image-based extraction of parameters, theoretical modeling, classification and co-registration and visualization module.

Monitorización y optimización de terapias de tele-neurorrehabilitación cognitiva

El Daño Cerebral Adquirido (DCA) se define como una lesión cerebral que ocurre después del nacimiento y que no guarda relación con defectos congénitos o enfermedades degenerativas. En el cerebro, se llevan a cabo las funciones mentales superiores como la atención, la memoria, las funciones ejecutivas y el lenguaje, consideradas pre-requisitos básicos de la inteligencia. Sea cual sea su causa, todo daño cerebral puede afectar a una o varias de estas funciones, de ahí la gravedad del problema. A pesar de los avances en nuevas técnicas de intervención precoz y el desarrollo de los cuidados intensivos, las afectaciones cerebrales aún no tienen tratamiento ni quirúrgico ni farmacológico que permita una restitución de las funciones perdidas. Los tratamientos de neurorrehabilitación cognitiva y funcional pretenden, por tanto, la minimización o compensación de las alteraciones ocasionadas por una lesión en el sistema nervioso. En concreto, la rehabilitación cognitiva se define como el proceso en el que personas que han sufrido un daño cerebral trabajan de manera conjunta con profesionales de la salud para remediar o aliviar los déficits cognitivos surgidos como consecuencia de un episodio neurológico. Esto se consigue gracias a la naturaleza plástica del sistema nervioso, donde el cerebro es capaz de reconfigurar sus conexiones neuronales, tanto creando nuevas como modificando las ya existentes. Durante los últimos años hemos visto una transformación de la sociedad, en lo que se ha denominado "sociedad de la información", cuyo pilar básico son las Tecnologías de la Información y las Comunicaciones (TIC). La aplicación de estas tecnologías en medicina ha revolucionado la manera en que se proveen los servicios sanitarios. Así, donde tecnología y medicina se mezclan, la telerrehabilitación se define como la rehabilitación a distancia, ayudando a extender los servicios de rehabilitación más allá de los centros hospitalarios, rompiendo las barreras geográficas, mejorando la eficiencia de los procesos y monitorizando en todo momento el estado y evolución del paciente. En este contexto, el objetivo general de la presente tesis es mejorar la rehabilitación neuropsicológica de pacientes que sufren alteraciones cognitivas, mediante el diseño, desarrollo y validación de un sistema de telemedicina que incorpora las TIC para avanzar hacia un nuevo paradigma personalizado, ubicuo y ecológico. Para conseguirlo, se han definido los siguientes objetivos específicos: • Analizar y modelar un sistema de telerrehabilitación, mediante la definición de objetivos y requisitos de usuario para diseñar las diferentes funcionalidades necesarias. • Definir una arquitectura de telerrehabilitación escalable para la prestación de diferentes servicios que agrupe las funcionalidades necesarias en módulos. • Diseñar y desarrollar la plataforma de telerrehabilitación, incluida la interfaz de usuario, creando diferentes roles de usuario con sus propias funcionalidades. • Desarrollar de un módulo de análisis de datos para extraer conocimiento basado en los resultados históricos de las sesiones de rehabilitación almacenadas en el sistema. • Evaluación de los resultados obtenidos por los pacientes después del programa de rehabilitación, obteniendo conclusiones sobre los beneficios del servicio implementado. • Evaluación técnica de la plataforma de telerrehabilitación, así como su usabilidad y la relación coste/beneficio. • Integración de un dispositivo de eye-tracking que permita la monitorización de la atención visual mientras los pacientes ejecutan tareas de neurorrehabilitación. •Diseño y desarrollo de un entorno de monitorización que permita obtener patrones de atención visual. Como resumen de los resultados obtenidos, se ha desarrollado y validado técnicamente la plataforma de telerrehabilitación cognitiva, demostrando la mejora en la eficiencia de los procesos, sin que esto resulte en una reducción de la eficacia del tratamiento. Además, se ha llevado a cabo una evaluación de la usabilidad del sistema, con muy buenos resultados. Respecto al módulo de análisis de datos, se ha diseñado y desarrollado un algoritmo que configura y planifica sesiones de rehabilitación para los pacientes, de manera automática, teniendo en cuenta las características específicas de cada paciente. Este algoritmo se ha denominado Intelligent Therapy Assistant (ITA). Los resultados obtenidos por el asistente muestran una mejora tanto en la eficiencia como en la eficacia de los procesos, comparado los resultados obtenidos con los de la planificación manual llevada a cabo por los terapeutas. Por último, se ha integrado con éxito el dispositivo de eye-tracking en la plataforma de telerrehabilitación, llevando a cabo una prueba con pacientes y sujetos control que ha demostrado la viabilidad técnica de la solución, así como la existencia de diferencias en los patrones de atención visual en pacientes con daño cerebral. ABSTRACT Acquired Brain Injury (ABI) is defined as brain damage that suddenly and unexpectedly appears in people’s life, being the main cause of disability in developed countries. The brain is responsible of the higher cognitive functions such as attention, memory, executive functions or language, which are considered basic requirements of the intelligence. Whatever its cause is, every ABI may affects one or several functions, highlighting the severity of the problem. New techniques of early intervention and the development of intensive ABI care have noticeably improved the survival rate. However, despite these advances, brain injuries still have no surgical or pharmacological treatment to re-establish lost functions. Cognitive rehabilitation is defined as a process whereby people with brain injury work together with health service professionals and others to remediate or alleviate cognitive deficits arising from a neurological insult. This is achieved by taking advantage of the plastic nature of the nervous system, where the brain can reconfigure its connections, both creating new ones, and modifying the previously existing. Neuro-rehabilitation aims to optimize the plastic nature by inducing a reorganization of the neural network, based on specific experiences. Personalized interventions from individual impairment profile will be necessary to optimize the remaining resources by potentiating adaptive responses and inhibiting maladaptive changes. In the last years, some applications and software programs have been developed to train or stimulate cognitive functions of different neuropsychological disorders, such as ABI, Alzheimer, psychiatric disorders, attention deficit or hyperactivity disorder (ADHD). The application of technologies into medicine has changed the paradigm. Telemedicine allows improving the quality of clinical services, providing better access to them and helping to break geographical barriers. Moreover, one of the main advantages of telemedicine is the possibility to extend the therapeutic processes beyond the hospital (e.g. patient's home). As a consequence, a reduction of unnecessary costs and a better costs/benefits ratio are achieved, making possible a more efficient use of the available resources In this context, the main objective of this work is to improve neuro-rehabilitation of patients suffering cognitive deficits, by designing, developing and validating a telemedicine system that incorporates ICTs to change this paradigm, making it more personalized, ubiquitous and ecologic. The following specific objectives have been defined: • To analyse and model a tele-rehabilitation system, defining objectives and user requirements to design the different needed functionalities. • To define a scalable tele-rehabilitation architecture to offer different services grouping functionalities into modules. • To design and develop the tele-rehabilitation platform, including the graphic user interface, creating different user roles and permissions. • To develop a data analysis module to extract knowledge based on the historic results from the rehabilitation sessions stored in the system. • To evaluate the obtained results by patients after the rehabilitation program, arising conclusions about the benefits of the implemented service. • To technically evaluate the tele-rehabilitation platform, and its usability and the costs/benefit ratio. • To integrate an eye-tracking device allowing the monitoring of the visual attention while patients execute rehabilitation tasks. •To design and develop a monitoring environment that allows to obtain visual attention patterns. Summarizing the obtained results, the cognitive tele-rehabilitation platform has been developed and evaluated technically, demonstrating the improvements on the efficiency without worsening the efficacy of the process. Besides, a usability evaluation has been carried out, with very good results. Regarding the data analysis module, an algorithm has been designed and developed to automatically select and configure rehabilitation sessions, taking into account the specific characteristics of each patient. This algorithm is called Intelligent Therapy Assistant (ITA). The obtained results show an improvement both in the efficiency and the efficacy of the process, comparing the results obtained by patients when they receive treatments scheduled manually by therapists. Finally, an eye-tracking device has been integrated in the tele-rehabilitation platform, carrying out a study with patients and control subjects demonstrating the technical viability of the developed monitoring environment. First results also show that there are differences between the visual attention patterns between ABI patients and control subjects.

Improving brain injury cognitive rehabilitation by personalized telerehabilitation services: Guttmann neuropersonal trainer

Cognitive rehabilitation aims to remediate or alleviate the cognitive deficits appearing after an episode of acquired brain injury (ABI). The purpose of this work is to describe the telerehabilitation platform called Guttmann Neuropersonal Trainer (GNPT) which provides new strategies for cognitive rehabilitation, improving efficiency and access to treatments, and to increase knowledge generation from the process. A cognitive rehabilitation process has been modeled to design and develop the system, which allows neuropsychologists to configure and schedule rehabilitation sessions, consisting of set of personalized computerized cognitive exercises grounded on neuroscience and plasticity principles. It provides remote continuous monitoring of patient's performance, by an asynchronous communication strategy. An automatic knowledge extraction method has been used to implement a decision support system, improving treatment customization. GNPT has been implemented in 27 rehabilitation centers and in 83 patients' homes, facilitating the access to the treatment. In total, 1660 patients have been treated. Usability and cost analysis methodologies have been applied to measure the efficiency in real clinical environments. The usability evaluation reveals a system usability score higher than 70 for all target users. The cost efficiency study results show a relation of 1-20 compared to face-to-face rehabilitation. GNPT enables brain-damaged patients to continue and further extend rehabilitation beyond the hospital, improving the efficiency of the rehabilitation process. It allows customized therapeutic plans, providing information to further development of clinical practice guidelines.

Aplicación móvil para el diagnóstico de cultivos

En este proyecto se estudiaron los requerimientos de diseño y desarrollo necesarios para la creación de una aplicación móvil basada en la identificación de las plagas (artrópodos) más importantes del cultivo del tomate en España. La aplicación (app) resuelve los problemas a los que se enfrentan agricultores profesionales y aficionados a la hora de identificar plagas, para ello la aplicación guía al usuario siguiendo una sencilla clave dicotómica hasta la identificación. Como valor añadido, la aplicación recomienda posibles tratamientos y medidas a seguir. La app además permite acceder a una página web que recogería información de interés del sector, así como la posibilidad de descargarla. La actualización de la información y el acceso a la web se realiza a través de la comunicación con un servidor web. Con este proyecto se busca facilitar la toma de decisión y minimizar el uso de recursos así como los costes económicos y ambientales. También se intenta contribuir a conseguir una agricultura más verde, responsable y sostenible, uno de los objetivos de la nueva PAC (2014-2020).

El cultivo de la stevia revolucionará la agricultura andaluza

La demanda creciente de los edulcorantes como sustitutos del azúcar y de los productos acalóricos o con bajas calorías de los consumidores españoles (entre 2008 y 2012 la demanda de edulcorantes creció en España un 36,5%) ha sido la base para la realización de este trabajo. En él se ha estudiado el cultivo de Stevia rebaudiana Bertoni, una planta compuesta originaria de Paraguay de la que se obtiene un edulcorante natural y acalórico del mismo nombre, stevia. Se ha analizado la viabilidad del cultivo en el sur de España, concretamente en Andalucía, mediante el estudio y la clasificación de una colección de más de 50 suelos de esta región según su índice de viabilidad para el cultivo de stevia. De este modo, se ha podido señalar la aptitud de las distintas comarcas de Andalucía para el cultivo de esta especie. De acuerdo con los resultados obtenidos, un 32% de las comarcas son de buena aptitud, un 53% son de aptitud media y un 15% son de baja aptitud para este cultivo.

Libro de Actas del VII Congreso de estudiantes de Ciencia, Tecnología e Ingeniería Agronómica

En este VII Congreso de estudiantes volvemos agradecer a todos los profesores y alumnos su participación y colaboración en todo momento para que en este Libro de Actas que tienes entre tus manos se hayan recopilado los trabajos de más de 100 estudiantes. Todos los trabajos han sido revisados por los profesores del Comité Científico del Congreso y esperamos que las correcciones hayan sido de utilidad a los autores. Ya sólo queda “la puesta en escena” con la exposición y los nervios de hablar en público. Se dice que “no hay temas aburridos, sino oradores poco entusiastas”. Sabemos que nuestros estudiantes, si se han lanzado a presentar su trabajo en este Congreso, es porque entusiasmo no les falta, y los organizadores del Congreso vamos a hacer todo lo posible para que no decaiga. No obstante, como en cualquier otro evento de este tipo, tenemos un tiempo limitado y esperamos que los ponentes controlen su entusiasmo y sepan respetarlo. Nuestro agradecimiento a la Fundación Premio Arce, a la Comunidad de Madrid a través del Proyecto MEDGAN (S2013/ABI-2913), a NANTA S.A., Dupont Industrial Biosciences, Editorial Agrícola Española, y a las Cátedras Fertiberia e Ingenio-UPM, como patrocinadores de este evento.

Multi-distributed wireless sensors for monitoring a long distance transport in a reefer container

The study of the temperature gradients in cold stores and containers is a critical issue in the food industry for the quality assurance of products during transport and for minimising losses. This work presents an analysis of the temperatures during the refrigerated transport of 4,320 kg of blueberries in a reefer (set point temperature at ?1ºC) on a container ship from Montevideo (Uruguay) to Verona (Italy). The monitoring was performed by using semi-passive RFID loggers (TurboTag cards). The objective was to carry out a multi-distributed supervision using low-cost, wireless and autonomous sensors for the characterisation of the distribution and spatial gradients of temperatures during a long distance transport. Data analysis shows spatial (phase space) and temporal sequencing diagrams and reveals a significant heterogeneity of temperature at different locations in the container, which highlights the ineffectiveness of a temperature control system based on a single sensor, as is usually done.

3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds

3D crop reconstruction with a high temporal resolution and by the use of non-destructive measuring technologies can support the automation of plant phenotyping processes. Thereby, the availability of such 3D data can give valuable information about the plant development and the interaction of the plant genotype with the environment. This article presents a new methodology for georeferenced 3D reconstruction of maize plant structure. For this purpose a total station, an IMU, and several 2D LiDARs with different orientations were mounted on an autonomous vehicle. By the multistep methodology presented, based on the application of the ICP algorithm for point cloud fusion, it was possible to perform the georeferenced point clouds overlapping. The overlapping point cloud algorithm showed that the aerial points (corresponding mainly to plant parts) were reduced to 1.5%–9% of the total registered data. The remaining were redundant or ground points. Through the inclusion of different LiDAR point of views of the scene, a more realistic representation of the surrounding is obtained by the incorporation of new useful information but also of noise. The use of georeferenced 3D maize plant reconstruction at different growth stages, combined with the total station accuracy could be highly useful when performing precision agriculture at the crop plant level.