Examinando por Autor "Ruíz-Salguero, Óscar"

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    Ítem
    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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    Ítem
    Spectral-based mesh segmentation
    (Springer Paris, 2016) Mejía, Daniel; Cadavid, Carlos A.; Ruíz-Salguero, Óscar; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
    In design and manufacturing, mesh segmentation is required for FACE construction in boundary representation (BRep), which in turn is central for featurebased design, machining, parametric CAD and reverse engineering, among others -- Although mesh segmentation is dictated by geometry and topology, this article focuses on the topological aspect (graph spectrum), as we consider that this tool has not been fully exploited -- We preprocess the mesh to obtain a edgelength homogeneous triangle set and its Graph Laplacian is calculated -- We then produce a monotonically increasing permutation of the Fiedler vector (2nd eigenvector of Graph Laplacian) for encoding the connectivity among part feature submeshes -- Within the mutated vector, discontinuities larger than a threshold (interactively set by a human) determine the partition of the original mesh -- We present tests of our method on large complex meshes, which show results which mostly adjust to BRep FACE partition -- The achieved segmentations properly locate most manufacturing features, although it requires human interaction to avoid over segmentation -- Future work includes an iterative application of this algorithm to progressively sever features of the mesh left from previous submesh removals