Robotic Exoskeleton Gait Training in Stroke: An Electromyography-Based Evaluation.

capacity score coherence electromyography exoskeletons rehabilitation robotics

Journal

Frontiers in neurorobotics
ISSN: 1662-5218
Titre abrégé: Front Neurorobot
Pays: Switzerland
ID NLM: 101477958

Informations de publication

Date de publication:
2021
Historique:
received: 30 06 2021
accepted: 08 10 2021
entrez: 13 12 2021
pubmed: 14 12 2021
medline: 14 12 2021
Statut: epublish

Résumé

The recovery of symmetric and efficient walking is one of the key goals of a rehabilitation program in patients with stroke. The use of overground exoskeletons alongside conventional gait training might help foster rhythmic muscle activation in the gait cycle toward a more efficient gait. About twenty-nine patients with subacute stroke have been recruited and underwent either conventional gait training or experimental training, including overground gait training using a wearable powered exoskeleton alongside conventional therapy. Before and after the rehabilitation treatment, we assessed: (i) gait functionality by means of clinical scales combined to obtain a Capacity Score, and (ii) gait neuromuscular lower limbs pattern using superficial EMG signals. Both groups improved their ability to walk in terms of functional gait, as detected by the Capacity Score. However, only the group treated with the robotic exoskeleton regained a controlled rhythmic neuromuscular pattern in the proximal lower limb muscles, as observed by the muscular activation analysis. Coherence analysis suggested that the control group (CG) improvement was mediated mainly by spinal cord control, while experimental group improvements were mediated by cortical-driven control. In subacute stroke patients, we hypothesize that exoskeleton multijoint powered fine control overground gait training, alongside conventional care, may lead to a more fine-tuned and efficient gait pattern.

Identifiants

pubmed: 34899227
doi: 10.3389/fnbot.2021.733738
pmc: PMC8663633
doi:

Types de publication

Journal Article

Langues

eng

Pagination

733738

Informations de copyright

Copyright © 2021 Longatelli, Pedrocchi, Guanziroli, Molteni and Gandolla.

Déclaration de conflit d'intérêts

AP and MG hold shares of AGADE Srl, Milano, Italy. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Valeria Longatelli (V)

NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

Alessandra Pedrocchi (A)

NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

Eleonora Guanziroli (E)

Villa Beretta Rehabilitation Center, Valduce Hospital, Costa Masnaga, Italy.

Franco Molteni (F)

Villa Beretta Rehabilitation Center, Valduce Hospital, Costa Masnaga, Italy.

Marta Gandolla (M)

Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.

Classifications MeSH