865 research outputs found
Upper-limb Kinematic Analysis and Artificial Intelligent Techniques for Neurorehabilitation and Assistive Environments
Stroke, one of the leading causes of death and disability around the
world, usually affects the motor cortex causing weakness or paralysis
in the limbs of one side of the body. Research efforts in neurorehabilitation
technology have focused on the development of robotic devices to
restore motor and cognitive function in impaired individuals, having
the potential to deliver high-intensity and motivating therapy.
End-effector-based devices have become an usual tool in the upper-
limb neurorehabilitation due to the ease of adapting to patients.
However, they are unable to measure the joint movements during
the exercise. Thus, the first part of this thesis is focused on the development
of a kinematic reconstruction algorithm that can be used
in a real rehabilitation environment, without disturbing the normal
patient-clinician interaction. On the basis of the algorithm found in
the literature that presents some instabilities, a new algorithm is developed.
The proposed algorithm is the first one able to online estimate
not only the upper-limb joints, but also the trunk compensation using
only two non-invasive wearable devices, placed onto the shoulder and
upper arm of the patient. This new tool will allow the therapist to perform
a comprehensive assessment combining the range of movement
with clinical assessment scales.
Knowing that the intensity of the therapy improves the outcomes of
neurorehabilitation, a ‘self-managed’ rehabilitation system can allow
the patients to continue the rehabilitation at home. This thesis proposes
a system to online measure a set of upper-limb rehabilitation gestures,
and intelligently evaluates the quality of the exercise performed by
the patients. The assessment is performed through the study of the
performed movement as a whole as well as evaluating each joint
independently. The first results are promising and suggest that this
system can became a a new tool to complement the clinical therapy at
home and improve the rehabilitation outcomes.
Finally, severe motor condition can remain after rehabilitation process.
Thus, a technology solution for these patients and people with
severe motor disabilities is proposed. An intelligent environmental
control interface is developed with the ability to adapt its scan control
to the residual capabilities of the user. Furthermore, the system estimates
the intention of the user from the environmental information and the behavior of the user, helping in the navigation through the
interface, improving its independence at home.El accidente cerebrovascular o ictus es una de las causas principales
de muerte y discapacidad a nivel mundial. Normalmente afecta a la
corteza motora causando debilidad o parálisis en las articulaciones del
mismo lado del cuerpo. Los esfuerzos de investigación dentro de la
tecnología de neurorehabilitación se han centrado en el desarrollo de
dispositivos robóticos para restaurar las funciones motoras y cognitivas
en las personas con esta discapacidad, teniendo un gran potencial
para ofrecer una terapia de alta intensidad y motivadora.
Los dispositivos basados en efector final se han convertido en una
herramienta habitual en la neurorehabilitación de miembro superior
ya que es muy sencillo adaptarlo a los pacientes. Sin embargo, éstos
no son capaces de medir los movimientos articulares durante la realización
del ejercicio. Por tanto, la primera parte de esta tesis se centra
en el desarrollo de un algoritmo de reconstrucción cinemática que
pueda ser usado en un entorno de rehabilitación real, sin perjudicar a
la interacción normal entre el paciente y el clínico. Partiendo de la base
que propone el algoritmo encontrado en la literatura, el cual presenta
algunas inestabilidades, se ha desarrollado un nuevo algoritmo. El
algoritmo propuesto es el primero capaz de estimar en tiempo real
no sólo las articulaciones del miembro superior, sino también la compensación
del tronco usando solamente dos dispositivos no invasivos
y portátiles, colocados sobre el hombro y el brazo del paciente. Esta
nueva herramienta permite al terapeuta realizar una valoración más
exhaustiva combinando el rango de movimiento con las escalas de
valoración clínicas.
Sabiendo que la intensidad de la terapia mejora los resultados de la
recuperación del ictus, un sistema de rehabilitación ‘auto-gestionado’
permite a los pacientes continuar con la rehabilitación en casa. Esta
tesis propone un sistema para medir en tiempo real un conjunto de
gestos de miembro superior y evaluar de manera inteligente la calidad
del ejercicio realizado por el paciente. La valoración se hace a través del
estudio del movimiento ejecutado en su conjunto, así como evaluando
cada articulación independientemente. Los primeros resultados son
prometedores y apuntan a que este sistema puede convertirse en una
nueva herramienta para complementar la terapia clínica en casa y
mejorar los resultados de la rehabilitación. Finalmente, después del proceso de rehabilitación pueden quedar
secuelas motoras graves. Por este motivo, se propone una solución
tecnológica para estas personas y para personas con discapacidades
motoras severas. Así, se ha desarrollado una interfaz de control de
entorno inteligente capaz de adaptar su control a las capacidades
residuales del usuario. Además, el sistema estima la intención del
usuario a partir de la información del entorno y el comportamiento del
usuario, ayudando en la navegación a través de la interfaz, mejorando
su independencia en el hogar
Daily-Life Monitoring of Stroke Survivors Motor Performance: The INTERACTION Sensing System
The objective of the INTERACTION Eu project is to develop and validate an unobtrusive and modular system for monitoring daily life activities, physical interactions with the environment and for training upper and lower extremity motor function in stroke subjects. This paper describes the development and preliminary testing of the project sensing platform made of sensing shirt, trousers, gloves and shoes. Modular prototypes were designed and built considering the minimal set of inertial, force and textile sensors that may enable an efficient monitoring of stroke patients. The single sensing elements are described and the results of their preliminary lab-level testing are reported
Biomedical Sensing and Imaging
This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor
Sensors for Robotic Hands: A Survey of State of the Art
Recent decades have seen significant progress in the field of artificial hands. Most of the
surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors.We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands
A review of the effectiveness of lower limb orthoses used in cerebral palsy
To produce this review, a systematic literature search was conducted for relevant articles published in the period between the date of the previous ISPO consensus conference report on cerebral palsy (1994) and April 2008. The search terms were 'cerebral and pals* (palsy, palsies), 'hemiplegia', 'diplegia', 'orthos*' (orthoses, orthosis) orthot* (orthotic, orthotics), brace or AFO
Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton
Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer
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