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Neuro-Mechanics of Recumbent Leg Cycling in Post-Acute Stroke Patients

Overview of attention for article published in Annals of Biomedical Engineering, June 2016
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Title
Neuro-Mechanics of Recumbent Leg Cycling in Post-Acute Stroke Patients
Published in
Annals of Biomedical Engineering, June 2016
DOI 10.1007/s10439-016-1660-0
Pubmed ID
Authors

Emilia Ambrosini, Cristiano De Marchis, Alessandra Pedrocchi, Giancarlo Ferrigno, Marco Monticone, Maurizio Schmid, Tommaso D’Alessio, Silvia Conforto, Simona Ferrante

Abstract

Cycling training is strongly applied in post-stroke rehabilitation, but how its modular control is altered soon after stroke has been not analyzed yet. EMG signals from 9 leg muscles and pedal forces were measured bilaterally during recumbent pedaling in 16 post-acute stroke patients and 12 age-matched healthy controls. Patients were asked to walk over a GaitRite mat and standard gait parameters were computed. Four muscle synergies were extracted through nonnegative matrix factorization in healthy subjects and patients unaffected legs. Two to four synergies were identified in the affected sides and the number of synergies significantly correlated with the Motricity Index (Spearman's coefficient = 0.521). The reduced coordination complexity resulted in a reduced biomechanical performance, with the two-module sub-group showing the lowest work production and mechanical effectiveness in the affected side. These patients also exhibited locomotor impairments (reduced gait speed, asymmetrical stance time, prolonged double support time). Significant correlations were found between cycling-based metrics and gait parameters, suggesting that neuro-mechanical quantities of pedaling can inform on walking dysfunctions. Our findings support the use of pedaling as a rehabilitation method and an assessment tool after stroke, mainly in the early phase, when patients can be unable to perform a safe and active gait training.

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Geographical breakdown

Country Count As %
Spain 1 <1%
Luxembourg 1 <1%
Unknown 176 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 24 13%
Student > Bachelor 23 13%
Researcher 22 12%
Student > Ph. D. Student 19 11%
Student > Doctoral Student 9 5%
Other 25 14%
Unknown 56 31%
Readers by discipline Count As %
Engineering 33 19%
Nursing and Health Professions 18 10%
Medicine and Dentistry 14 8%
Neuroscience 13 7%
Sports and Recreations 12 7%
Other 23 13%
Unknown 65 37%