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Magdo Bortole

Researcher at Spanish National Research Council

Publications -  9
Citations -  339

Magdo Bortole is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Exoskeleton & Rehabilitation. The author has an hindex of 5, co-authored 9 publications receiving 249 citations. Previous affiliations of Magdo Bortole include Cajal Institute & University of Houston.

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Journal ArticleDOI

The H2 robotic exoskeleton for gait rehabilitation after stroke: early findings from a clinical study

TL;DR: The developed exoskeleton enables longitudinal overground training of walking in hemiparetic patients after stroke and is robust and safe when applied to assist a stroke patient performing an overground walking task.
Journal ArticleDOI

Neural Decoding of Robot-Assisted Gait During Rehabilitation After Stroke.

TL;DR: This study investigates the feasibility of decoding gait kinematics from chronic stroke patients undergoing gait rehabilitation based on a lower extremity gait system (H2 NeuroExo) integrated with a noninvasive neural interface based on electroencephalography (EEG).
Journal ArticleDOI

Inertial Sensor Error Reduction through Calibration and Sensor Fusion

TL;DR: This paper presents the comparison between cooperative and local Kalman Filters for estimating the absolute segment angle, under two calibration conditions, and indicates that regardless of the segment and filter applied, the more complex calibration always results in a significantly better performance compared to the simplified calibration.
Book ChapterDOI

Development of a Exoskeleton for Lower Limb Rehabilitation

TL;DR: A robotic platform aimed to assist overground gait training for disabled people with a six degree of freedom device that can replicate a normal gait pattern for walk assistance is presented.
Journal ArticleDOI

Global Kalman filter approaches to estimate absolute angles of lower limb segments

TL;DR: A novel global KF relying only on inertial sensor data is presented, and a previously presented global approach fusing inertial sensors data with data from exoskeleton encoders, which was superior to local KFs.