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.

A novel motion tracking system for evaluation of functional rehabilitation of the upper limbs

Upper limb function impairment is one of the most common sequelae of central nervous system injury, especially in stroke patients and when spinal cord injury produces tetraplegia. Conventional assessment methods cannot provide objective evaluation of patient performance and the tiveness of therapies. The most common assessment tools are based on rating scales, which are inefficient when measuring small changes and can yield subjective bias. In this study, we designed an inertial sensor-based monitoring system composed of five sensors to measure and analyze the complex movements of the upper limbs, which are common in activities of daily living. We developed a kinematic model with nine degrees of freedom to analyze upper limb and head movements in three dimensions. This system was then validated using a commercial optoelectronic system. These findings suggest that an inertial sensor-based motion tracking system can be used in patients who have upper limb impairment through data integration with a virtual reality-based neuroretation system.

A data-globe and immersive virtual reality environment for upper limb rehabilitation after spinal cord injury

While a number of virtual data-gloves have been used in stroke, there is little evidence about their use in spinal cord injury (SCI). A pilot clinical experience with nine SCI subjects was performed comparing two groups: one carried out a virtual rehabilitation training based on the use of a data glove, CyberTouch combined with traditional rehabilitation, during 30 minutes a day twice a week along two weeks; while the other made only conventional rehabilitation. Furthermore, two functional indexes were developed in order to assess the patient’s performance of the sessions: normalized trajectory lengths and repeatability. While differences between groups were not statistically significant, the data-glove group seemed to obtain better results in the muscle balance and functional parameters, and in the dexterity, coordination and fine grip tests. Related to the indexes that we implemented, normalized trajectory lengths and repeatability, every patient showed an improvement in at least one of the indexes, either along Y-axis trajectory or Z-axis trajectory. This study might be a step in investigating new ways of treatments and objective measures in order to obtain more accurate data about the patient’s evolution, allowing the clinicians to develop rehabilitation treatments, adapted to the abilities and needs of the patients.

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.

MedVir: an interactive representation system of multidimensional medical data applied to Traumatic Brain Injury's rehabilitation prediction

Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.

Non-invasive user monitoring in daily tasks for cognitive rehabilitation

El uso de técnicas para la monitorización del movimiento humano generalmente permite a los investigadores analizar la cinemática y especialmente las capacidades motoras en aquellas actividades de la vida cotidiana que persiguen un objetivo concreto como pueden ser la preparación de bebidas y comida, e incluso en tareas de aseo. Adicionalmente, la evaluación del movimiento y el comportamiento humanos en el campo de la rehabilitación cognitiva es esencial para profundizar en las dificultades que algunas personas encuentran en la ejecución de actividades diarias después de accidentes cerebro-vasculares. Estas dificultades están principalmente asociadas a la realización de pasos secuenciales y al reconocimiento del uso de herramientas y objetos. La interpretación de los datos sobre la actitud de este tipo de pacientes para reconocer y determinar el nivel de éxito en la ejecución de las acciones, y para ampliar el conocimiento en las enfermedades cerebrales, sus consecuencias y severidad, depende totalmente de los dispositivos usados para la captura de esos datos y de la calidad de los mismos. Más aún, existe una necesidad real de mejorar las técnicas actuales de rehabilitación cognitiva contribuyendo al diseño de sistemas automáticos para crear una especie de terapeuta virtual que asegure una vida más independiente de estos pacientes y reduzca la carga de trabajo de los terapeutas. Con este objetivo, el uso de sensores y dispositivos para obtener datos en tiempo real de la ejecución y estado de la tarea de rehabilitación es esencial para también contribuir al diseño y entrenamiento de futuros algoritmos que pudieran reconocer errores automáticamente para informar al paciente acerca de ellos mediante distintos tipos de pistas como pueden ser imágenes, mensajes auditivos o incluso videos. La tecnología y soluciones existentes en este campo no ofrecen una manera totalmente robusta y efectiva para obtener datos en tiempo real, por un lado, porque pueden influir en el movimiento del propio paciente en caso de las plataformas basadas en el uso de marcadores que necesitan sensores pegados en la piel; y por otro lado, debido a la complejidad o alto coste de implantación lo que hace difícil pensar en la idea de instalar un sistema en el hospital o incluso en la casa del paciente. Esta tesis presenta la investigación realizada en el campo de la monitorización del movimiento de pacientes para proporcionar un paso adelante en términos de detección, seguimiento y reconocimiento del comportamiento de manos, gestos y cara mediante una manera no invasiva la cual puede mejorar la técnicas actuales de rehabilitación cognitiva para la adquisición en tiempo real de datos sobre el comportamiento del paciente y la ejecución de la tarea. Para entender la importancia del marco de esta tesis, inicialmente se presenta un resumen de las principales enfermedades cognitivas y se introducen las consecuencias que tienen en la ejecución de tareas de la vida diaria. Más aún, se investiga sobre las metodologías actuales de rehabilitación cognitiva. Teniendo en cuenta que las manos son la principal parte del cuerpo para la ejecución de tareas manuales de la vida cotidiana, también se resumen las tecnologías existentes para la captura de movimiento de manos. Una de las principales contribuciones de esta tesis está relacionada con el diseño y evaluación de una solución no invasiva para detectar y seguir las manos durante la ejecución de tareas manuales de la vida cotidiana que a su vez involucran la manipulación de objetos. Esta solución la cual no necesita marcadores adicionales y está basada en una cámara de profundidad de bajo coste, es robusta, precisa y fácil de instalar. Otra contribución presentada se centra en el reconocimiento de gestos para detectar el agarre de objetos basado en un sensor infrarrojo de última generación, y también complementado con una cámara de profundidad. Esta nueva técnica, y también no invasiva, sincroniza ambos sensores para seguir objetos específicos además de reconocer eventos concretos relacionados con tareas de aseo. Más aún, se realiza una evaluación preliminar del reconocimiento de expresiones faciales para analizar si es adecuado para el reconocimiento del estado de ánimo durante la tarea. Por su parte, todos los componentes y algoritmos desarrollados son integrados en un prototipo simple para ser usado como plataforma de monitorización. Se realiza una evaluación técnica del funcionamiento de cada dispositivo para analizar si es adecuada para adquirir datos en tiempo real durante la ejecución de tareas cotidianas reales. Finalmente, se estudia la interacción con pacientes reales para obtener información del nivel de usabilidad del prototipo. Dicha información es esencial y útil para considerar una rehabilitación cognitiva basada en la idea de instalación del sistema en la propia casa del paciente al igual que en el hospital correspondiente. ABSTRACT The use of human motion monitoring techniques usually let researchers to analyse kinematics, especially in motor strategies for goal-oriented activities of daily living, such as the preparation of drinks and food, and even grooming tasks. Additionally, the evaluation of human movements and behaviour in the field of cognitive rehabilitation is essential to deep into the difficulties some people find in common activities after stroke. This difficulties are mainly associated with sequence actions and the recognition of tools usage. The interpretation of attitude data of this kind of patients in order to recognize and determine the level of success of the execution of actions, and to broaden the knowledge in brain diseases, consequences and severity, depends totally on the devices used for the capture of that data and the quality of it. Moreover, there is a real need of improving the current cognitive rehabilitation techniques by contributing to the design of automatic systems to create a kind of virtual therapist for the improvement of the independent life of these stroke patients and to reduce the workload of the occupational therapists currently in charge of them. For this purpose, the use of sensors and devices to obtain real time data of the execution and state of the rehabilitation task is essential to also contribute to the design and training of future smart algorithms which may recognise errors to automatically provide multimodal feedback through different types of cues such as still images, auditory messages or even videos. The technology and solutions currently adopted in the field don't offer a totally robust and effective way for obtaining real time data, on the one hand, because they may influence the patient's movement in case of marker-based platforms which need sensors attached to the skin; and on the other hand, because of the complexity or high cost of implementation, which make difficult the idea of installing a system at the hospital or even patient's home. This thesis presents the research done in the field of user monitoring to provide a step forward in terms of detection, tracking and recognition of hand movements, gestures and face via a non-invasive way which could improve current techniques for cognitive rehabilitation for real time data acquisition of patient's behaviour and execution of the task. In order to understand the importance of the scope of the thesis, initially, a summary of the main cognitive diseases that require for rehabilitation and an introduction of the consequences on the execution of daily tasks are presented. Moreover, research is done about the actual methodology to provide cognitive rehabilitation. Considering that the main body members involved in the completion of a handmade daily task are the hands, the current technologies for human hands movements capture are also highlighted. One of the main contributions of this thesis is related to the design and evaluation of a non-invasive approach to detect and track user's hands during the execution of handmade activities of daily living which involve the manipulation of objects. This approach does not need the inclusion of any additional markers. In addition, it is only based on a low-cost depth camera, it is robust, accurate and easy to install. Another contribution presented is focused on the hand gesture recognition for detecting object grasping based on a brand new infrared sensor, and also complemented with a depth camera. This new, and also non-invasive, solution which synchronizes both sensors to track specific tools as well as recognize specific events related to grooming is evaluated. Moreover, a preliminary assessment of the recognition of facial expressions is carried out to analyse if it is adequate for recognizing mood during the execution of task. Meanwhile, all the corresponding hardware and software developed are integrated in a simple prototype with the purpose of being used as a platform for monitoring the execution of the rehabilitation task. Technical evaluation of the performance of each device is carried out in order to analyze its suitability to acquire real time data during the execution of real daily tasks. Finally, a kind of healthcare evaluation is also presented to obtain feedback about the usability of the system proposed paying special attention to the interaction with real users and stroke patients. This feedback is quite useful to consider the idea of a home-based cognitive rehabilitation as well as a possible hospital installation of the prototype.

Persuasive system design methods to deliver a cardio-rehabilitation program of post mi outpatients

Las enfermedades no transmisibles provocan cada ano 38 millones de fallecimientos en el mundo. Entre ellas, tan solo cuatro enfermedades son responsables del 82% de estas muertes: las enfermedades cardiovasculares, las enfermedades crónicas respiratorias, la diabetes, y el cáncer. Se prevé que estas cifras aumenten en los próximos anos, ya que las tendencias indican que en el año 2030 las muertes por esta causa ascenderán a 53 millones de personas. La Organización Mundial de la Salud (OMS) considera importante buscar soluciones para afrontar esta situación y ha solicitado a los gobiernos del mundo la implementación de intervenciones para mejorar los hábitos de vida de las personas y reducir así el riesgo de desarrollo de enfermedades no trasmisibles. Cada año se producen 32 millones de infartos de miocardio y derrames celebrales, de los cuales 12.5 son mortales. En el mundo entre el 40% y 75% de la víctimas de un infarto de miocardio mueren antes de su ingreso en el hospital. En los casos que sobreviven, la adopción de un estilo de vida saludable puede evitar infartos sucesivo, y supone un ahorro potencial de 6 billones de euros al año. La rehabilitación cardiaca es un programa individualizado que aplica un método multidisciplinar para ayudar al paciente a recuperar su condición física, a gestionar la enfermedad cardiovascular y sus comorbilidades, a adoptar hábitos de vida saludables, y a promover su salud mental. La rehabilitación cardiaca requiere la total involucración y motivación del paciente, solo de esta manera se podrán promover hábitos saludables y mejorar la gestión y prevención de su enfermedad. Aunque la participación en los programas de rehabilitación cardiaca es baja, hoy en día existen programas de rehabilitación cardiaca que el paciente puede realizar en su casa. Estos suponen una solución prometedora para aumentar la participación. La rehabilitación cardiaca se considera una intervención integral donde los modelos de psicología de la salud son aplicados para promover un cambio en el estilo de vida de las personas así como para ayudarles a afrontar su propia enfermedad. Existen métodos para implementar cambios de hábitos y de aptitud, y también se considera muy relevante promover no solo el bienestar físico sino también el mental. Existen tecnologías que promueven los cambios de comportamientos en los seres humanos. En concreto, las tecnologías persuasivas y los sistemas de apoyo al cambio de comportamientos modelan las características, las estrategias y los métodos de diseño para promover cambios usando la tecnología. Pero estos modelos tienen algunas limitaciones: todavía no se ha definido que rol tienen las emociones en el cambio de comportamientos y como traducir los métodos de la psicología de la salud en la tecnología. Esta tesis se centra en tres elementos que tienen un rol clave en los cambios de hábitos y actitud: el estado físico, el estado mental, y la tecnología. -Estado de salud: un estado de salud critico puede modificar la actitud del ser humano respecto al cambio. A la vez un buen estado de salud hace que la necesidad del cambio sea menos percibida. -Estado emocional: la actitud tiene un componente afectivo. Los estados emocionales negativos pueden reducir la habilidad de una persona para adoptar nuevos comportamientos. La salud mental es la situación ideal donde los individuos tienen predisposición a los cambios. La tecnología puede ayudar a las personas a adoptar nuevos hábitos, así como a mantener una salud física y mental. Este trabajo de investigación se centra en el diseño de tecnologías para la mejora del estado físico y emocional de las personas. Se ha propuesto un marco de diseño llamado “Well.Be.Sign”. El marco se basa en tres aspectos: El marco teórico: representa los elementos que se tienen que definir para diseñar tecnologías para promover el bienestar de las personas. -El diagrama de influencia: presenta las fuerzas de ‘persuasión’ en el contexto de la salud. El rol de las tecnologías persuasivas ha sido contextualizado en una dimensión donde otros elementos influencian el usuario.  El proceso de diseño: describe el proceso de diseño utilizando una metodología iterativa e incremental que aplica una combinación de métodos de diseño existentes (Diseño Orientado a Objetivos, Diseño de Sistemas Persuasivos) así como elementos originales de este trabajo de investigación. Los métodos se han aplicados para diseñar un sistema que ofrezca un programa de tele-rehabilitación cardiaca. Inicialmente se ha diseñado un prototipo de acuerdo con las necesidades del usuario. En segundo lugar, el prototipo se ha extendido especificando la intervención requerida para al programa de rehabilitación cardiaca. Finalmente el sistema se ha desarrollado y validado en un ensayo clínico con grupo control, donde se observaron las variaciones del estado cardiovascular, el nivel de conocimiento acerca de la enfermedad, la percepción de la enfermedad, la persistencia de hábitos saludables, y la aceptabilidad del sistema. Los resultados muestran que el grupo de intervención tiene una superior capacidad cardiovascular, mejor conocimiento acerca de la enfermedad, y más percepción de control de la enfermedad. Asimismo, en algunos casos se ha registrado persistencia de los hábitos de ejercicios 6 meses después del uso del sistema. Otros dos estudios se han presentado para demonstrar la relevancia del estado emocional del usuario en el diseño de aplicaciones para la promoción del bienestar.  En personas con una grave enfermedad crónica como la insuficiencia cardiaca, donde se ha presentado las conexiones entre estado de salud y estado emocional. En el estudio se ensena la relaciones que tienen los síntomas y las emociones negativas y como un estado negativo emocional puede empeorar la condición física del paciente. -Personas con trastornos del humor: el estudio muestra como las emociones pueden tener un impacto en la percepción de la tecnología por parte del usuario. ABSTRACT Noncommunicable diseases (NCDs) cause the death of 38 million people every year. Four major NCDs are responsible for 82% of these deaths: cardio vascular disease, chronic respiratory disease, diabetes and cancer. These pandemic numbers are projected to raise to 53 million deaths in 2030, and for this reason the assembly of the World Health Organization (WHO) considers communicable diseases as an urgent need to be addressed. It is also a trend to advocate the adoption of mobile technology to deliver health services and to promote healthy behaviours among citizens, but adopting healthS promoting lifestyle is still a difficult task facing human tendencies. Within this context, there is a promising opportunity: persuasive technologies. These technologies are intentionally designed to change a person’s attitudes or behaviours; when applied in this context, than can be used to change health-related attitudes, beliefs, and behaviours. Each year there are 32 million heart attacks and strokes globally, of which about 12.5 million are fatal. Worldwide between 40 and 75% of all heart-attack victims die before reaching hospital. Avoiding a second heart attack by improving adherence to lifestyle and medication regimens has a cost saving potential of around €6 billion per year. In most of the cases the cardiovascular event has been provoked by unhealthy lifestyle. Furthermore, after an MI event the patient's decision to adopt or not healthier behaviour will influence the progress of the disease. Cardio-rehabilitation is an individualized program that follows a multidisciplinary approach to support the user to recover from the Myocardial Infarction, manage the Cardio Vascular Disease and the comorbidities, adopt healthy habits, and cope with any emotional distress. Cardio- rehabilitation requires patient participation and willingness to perform behavioral modifications and change the attitude toward the management and prevention of the disease. Participation in the Cardio Rehabilitation program is not high; the home-based rehabilitation program is a promising solution to increase participation. Nowadays cardio rehabilitation is considered a comprehensive intervention in which models of health psychology are applied to promote the behaviour change of the individuals. Relevant methods that have been successfully applied to foster healthy habits include the Health Belief Model and the Trans Theoretical Model. Studies also demonstrate the importance to promote not only the physical but also the mental well being of the individuals. The idea of also promoting behaviour change using technologies has been defined by the literature as persuasive technologies or behaviour change support systems, in which the features, the strategies and the design method have been modelled to foster the behaviour change using technology. Limitations have been found in this model: there is still research to be done on the role of the emotions and how psychological health intervention can be translated into computer methods. This research focuses on three elements that could foster behaviour change in individuals: the physical and emotional status of the person, and the technology. Every component can influence the user's attitude and behaviour in the following ways: ' Physical status: bad physical status could change human attitude toward the necessity to adopt health behaviours; at the same time, good health status reduces the need to adopt healthy habits. ' Emotional status: the attitude has an affective component, negative emotional state can reduce the ability of a person to adopt new behaviours, and mental well being is the ideal situation in which individuals have a predisposition to adopt healthy behaviours. ' Technology: it can help users to adopt new behaviours and can also be support to promote physical and emotional status. Following this approach the idea driven in this research is that technology that is designed to improve the physical status and the emotional status of the individual could better foster behaviour change. According to this principle, the Well.Be.Sign framework has been proposed. The framework is based on three views: ' The theoretical framework: it represents the patterns that have to be defined to design the technologies to promote well being. ' The influence diagram: it shows the persuasive forces in the context of health care. The role of the persuasive technologies is contextualized in a wider universe where other factors and persuasive forces influence a patient. ' The design process: it shows the process of design using an iterative, incremental methodology that applies a combination of existing methodologies (Goal Directed Design and Persuasive System Design) and others that are original to this research. The methods have been applied to design a system to deliver cardio rehabilitation at home: first a prototype has been defined according to the user’s needs, then it has been extended with the specific intervention required for the cardio–rehabilitation, finally the system has been developed and validated in a controlled clinical study in which the cardiovascular fitness, the level of knowledge, the perception of the illness, the persistence of healthy habits and the system acceptance (only the intervention group) were measured. The results show that the intervention group increased cardiovascular capacity, knowledge, feeling of control of illness and perceived benefits of exercise at the end of the study. After six months of the study, a followSup of the exercise habits was performed. Some individuals of the intervention group continued to be engaged in the running exercise sessions promoted in the designed system. Two other cases have been presented to demonstrate the foundations of the Well.Be.Sign’s approach to promote both physical and emotional status: ' People affected by Heart Failure, in which a bidirectional connection between health status and emotions has been discussed with patients. Two correlations were demonstrated: the relationship between symptoms and negative emotional response, and that negative emotional status is correlated with worsening of chronic conditions. ' People with mood disorders: the study shows that emotions could also impact how the user perceives the technology.