Literature DB >> 20870498

Electromyographic patterns and knee joint kinematics during walking at various speeds.

L Arendt-Nielsen1, T Sinkjær, J Nielsen, K Kallesøe.   

Abstract

We investigated surface electromyographic (EMG) patterns and knee joint kinematics when the speed of walking was increased from 3.5 to 5.0 km/h on horizontal and inclined (25%) treadmill. The EMG signals from six lower limb muscles (sartorius, gluteus maximus, biceps femoris, vastus lateralis, gastrocnemius lateralis, and tibialis anterior) were recorded from 10 subjects. The knee joint flexion/extension angle was measured by a goniometer. The mean EMG amplitudes, peak EMG amplitude, and EMG profiles were calculated for the muscles for each stride, together with the knee joint angle, knee joint angular velocity, and knee joint angular acceleration. The modulations created in the mean and peak EMG amplitudes by changing the walking speed were different for the muscles tested. The peak knee joint angle, velocity, and acceleration were enhanced as the walking speed was increased. An increase in speed for both horizontal and uphill walking did not change the shape of the EMG profile or the shape of the knee joint kinematic profiles, indicating that the central nervous system (CNS) tends to maintain the shape of the knee joint movement pattern despite substantial changes in EMG amplitude.
Copyright © 1991. Published by Elsevier Ltd.

Year:  1991        PMID: 20870498     DOI: 10.1016/1050-6411(91)90002-M

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  5 in total

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Authors:  Jason R Franz; Rodger Kram
Journal:  Gait Posture       Date:  2011-10-02       Impact factor: 2.840

4.  Detailed spatial characterization of superficial hip muscle activation during walking: A multi-electrode surface EMG investigation of the gluteal region in healthy older adults.

Authors:  Christoph Anders; Steffen Patenge; Klaus Sander; Frank Layher; Uta Biedermann; Raimund W Kinne
Journal:  PLoS One       Date:  2017-06-05       Impact factor: 3.240

5.  An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters.

Authors:  Tiwana Varrecchia; Stefano Filippo Castiglia; Alberto Ranavolo; Carmela Conte; Antonella Tatarelli; Gianluca Coppola; Cherubino Di Lorenzo; Francesco Draicchio; Francesco Pierelli; Mariano Serrao
Journal:  PLoS One       Date:  2021-02-19       Impact factor: 3.240

  5 in total

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