Examinando por Autor "Flórez, Julián"

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    Inverse kinematics for upper limb compound movement estimation in exoskeleton-assisted rehabilitation
    (Hindawi Publishing Corp., 2016-05-16) Cortés, Camilo; De los Reyes-Guzmán, Ana; Scorza, Davide; Bertelsen, Álvaro; Carrasco, Eduardo; Gil-Agudo, Ángel; Ruíz-Salguero, Óscar; Flórez, Julián; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
    Robot-Assisted Rehabilitation (RAR) is relevant for treating patients affected by nervous system injuries (e.g., stroke and spinal cord injury) -- The accurate estimation of the joint angles of the patient limbs in RAR is critical to assess the patient improvement -- The economical prevalent method to estimate the patient posture in Exoskeleton-based RAR is to approximate the limb joint angles with the ones of the Exoskeleton -- This approximation is rough since their kinematic structures differ -- Motion capture systems (MOCAPs) can improve the estimations, at the expenses of a considerable overload of the therapy setup -- Alternatively, the Extended Inverse Kinematics Posture Estimation (EIKPE) computational method models the limb and Exoskeleton as differing parallel kinematic chains -- EIKPE has been tested with single DOFmovements of the wrist and elbow joints -- This paper presents the assessment of EIKPEwith elbow-shoulder compoundmovements (i.e., object prehension) -- Ground-truth for estimation assessment is obtained from an optical MOCAP (not intended for the treatment stage) -- The assessment shows EIKPE rendering a good numerical approximation of the actual posture during the compoundmovement execution, especially for the shoulder joint angles -- This work opens the horizon for clinical studies with patient groups, Exoskeleton models, and movements types --
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    Robust CT to US 3D-3D Registration by Using Principal Component Analysis and Kalman Filtering
    (2016-07) Echeverría, Rebeca; Cortes, Camilo; Bertelsen, Alvaro; Macia, Ivan; Ruíz, Óscar E.; Flórez, Julián; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
    Algorithms based on the unscented Kalman filter (UKF) have been proposed as an alternative for registration of point clouds obtained from vertebral ultrasound (US) and computerised tomography (CT) scans, effectively handling the US limited depth and low signaltonoise ratio -- Previously proposed methods are accurate, but their convergence rate is considerably reduced with initial misalignments of the datasets greater than or 30 mm -- We propose a novel method which increases robustness by adding a coarse alignment of the datasets’ principal components and batchbased point inclusions for the UKF -- Experiments with simulated scans with full coverage of a single vertebra show the method’s capability and accuracy to correct misalignments as large as and 90 mm -- Furthermore, the method registers datasets with varying degrees of missing data and datasets with outlier points coming from adjacent vertebrae