3 results match your criteria: "Escuela Universitaria de Fisioterapia de la ONCE[Affiliation]"

Article Synopsis
  • The study examines how deep learning can compare gait cycle time series from healthy children assessed in two different labs using similar protocols.
  • Researchers used a ResNet-based model that effectively identified the source of each dataset with high accuracy by analyzing various gait parameters.
  • The findings highlight the need for standardized protocols and effective data pre-processing to improve the consistency and applicability of machine learning models in clinical environments.
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Background And Purpose: The exercise training included in pulmonary rehabilitation (PR) programmes improves exercise capacity and quality of life in patients with chronic obstructive pulmonary disease (COPD). Nevertheless, the duration of these effects is limited, and the implementation of PR is still insufficient. Moreover, the physical activity level of COPD patients is low, and it is not modified with the classic PR programmes.

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Characterizing Normal and Pathological Gait through Permutation Entropy.

Entropy (Basel)

January 2018

MOVUAM-TRADESMA laboratory, Department of Anatomy, Histology and Neuroscience, Universidad Autónoma de Madrid, IdiPaz, 28029 Madrid, Spain.

Cerebral palsy is a physical impairment stemming from a brain lesion at perinatal time, most of the time resulting in gait abnormalities: the first cause of severe disability in childhood. Gait study, and instrumental gait analysis in particular, has been receiving increasing attention in the last few years, for being the complex result of the interactions between different brain motor areas and thus a proxy in the understanding of the underlying neural dynamics. Yet, and in spite of its importance, little is still known about how the brain adapts to cerebral palsy and to its impaired gait and, consequently, about the best strategies for mitigating the disability.

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