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Doctoral Thesis
DOI
10.11606/T.18.2015.tde-19052015-172242
Document
Author
Full name
Samuel Lourenço Nogueira
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2015
Supervisor
Committee
Siqueira, Adriano Almeida Gonçalves (President)
Frizera Neto, Anselmo
Lima, Eduardo Rocon de
Montagnoli, Arlindo Neto
Terra, Marco Henrique
Title in Portuguese
Sistemas Markovianos para estimativa de ângulos absolutos em exoesqueletos de membros inferiores
Keywords in Portuguese
Exoesqueletos
Filtro de Kalman
Filtro robusto
Ortheses
Saltos Markovianos
Sistemas lineares
Tempo discreto
Abstract in Portuguese
Nesta tese de doutorado são apresentados sistemas globais de estimativa baseados em modelos Markovianos aplicados na área de reabilitação robótica. Os sistemas propostos foram desenvolvidos para estimar as posições angulares dos elos de exoesqueletos para membros inferiores, desenvolvidos para reabilitação motora em pacientes que sofreram Acidente Vascular Cerebral (AVC) ou lesão medular. Filtros baseados no filtro de Kalman, um nominal e outro considerando incertezas no modelo, foram utilizados em estratégias de fusão de dados de sensores provenientes de sensores inerciais, possibilitando estimativas de posicionamentos angulares. Algoritmos genéticos são utilizados na otimização dos filtros, ajustando as matrizes de peso destes. Em oposição as modelagens tradicionais, via estimativa local, utilizando somente uma unidade inercial para cada modelo, propõe-se um sistema global de estimativa, obtendo-se a melhor informação de cada sensor combinando-os em um modelo Markoviano. Resultados experimentais com um exoesqueleto foram utilizados para comparar a abordagem Markoviana às convencionais.
Title in English
Markovians systems to estimate absolute angles in lower limb exoskeletons
Keywords in English
Discrete-time systems
Exoskeleton
Kalman filter
Linear systems
Markovian jump
Ortheses
Robust filter
Abstract in English
In this thesis are presented global estimation systems based on Markov models applied in robotic rehabilitation area. The proposed systems have been developed to estimate the angular positions of the exoskeletons for lower limbs, designed to provide motor rehabilitation of stroke and spinal cord injured people. Filters based on the Kalman filter, one nominal and other considering uncertainties in the model, were used in sensor data fusion strategies from inertial sensors, to estimate angular positions. Genetic algorithms are used to the optimization of filters, tuning the weighting matrices. In opposition to these modelling via local estimation, using only one inertial unit, we also chose a global modelling getting the best information from each sensor, combining them in a Markov model. Experimental results with an exoskeleton were used to compare the Markovian approach to conventional.
 
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NOGUEIRA_SL_2015.pdf (14.38 Mbytes)
Publishing Date
2015-06-01
 
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