Literature DB >> 30843847

Asymmetry Index in Muscle Activations.

C Castagneri, V Agostini, S Rosati, G Balestra, M Knaflitz.   

Abstract

Gait asymmetry is typically evaluated using spatio-temporal or joint kinematics parameters. Only a few studies addressed the problem of defining an asymmetry index directly based on muscle activity, extracting parameters from surface electromyography (sEMG) signals. Moreover, no studies used the extraction of the muscle principal activations (activations that are necessary for accomplishing a specific motor task) as the base to construct an asymmetry index, less affected by the variability of sEMG patterns. The aim of this paper is to define a robust index to quantitatively assess the asymmetry of muscle activations during locomotion, based on the extraction of the principal activations. SEMG signals were analyzed combining statistical gait analysis (SGA) and a clustering algorithm that allows for obtaining the muscle principal activations. We evaluated the asymmetry levels of four lower limb muscles in: (1) healthy subjects of different ages (children, adults, and elderly); (2) different populations of orthopedic patients (adults with megaprosthesis of the knee after bone tumor resection, elderly subjects after total knee arthroplasty, and elderly subjects after total hip arthroplasty); and (3) neurological patients (children with hemiplegic cerebral palsy and elderly subjects affected by idiopathic normal pressure hydrocephalus). The asymmetry index obtained for each pathological population was then compared to that of age-matched controls. We found asymmetry levels consistent with the expected impact of the different pathologies on muscle activation during gait. This suggests that the proposed index can be successfully used in clinics for an objective assessment of the muscle activation asymmetry during locomotion.

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Year:  2019        PMID: 30843847     DOI: 10.1109/TNSRE.2019.2903687

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  2 in total

1.  Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals.

Authors:  Francesco Di Nardo; Christian Morbidoni; Guido Mascia; Federica Verdini; Sandro Fioretti
Journal:  Biomed Eng Online       Date:  2020-07-28       Impact factor: 2.819

2.  Critical Issues and Imminent Challenges in the Use of sEMG in Return-To-Work Rehabilitation of Patients Affected by Neurological Disorders in the Epoch of Human-Robot Collaborative Technologies.

Authors:  Alberto Ranavolo; Mariano Serrao; Francesco Draicchio
Journal:  Front Neurol       Date:  2020-12-22       Impact factor: 4.003

  2 in total

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