Literature DB >> 10200405

Electromyography in the biomechanical analysis of human movement and its clinical application.

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Abstract

This article introduces the area of scientific study of human movement. It is primarily intended for readers who wish to form a judgement on the usefulness of scientific movement analysis techniques in the treatment process of patients with abnormal movement patterns. With a focus on the analysis of human locomotion, the paper outlines the historical development of a biomechanical approach towards the understanding of human movement patterns. This approach alone proves to be inadequate in supplying reliable information on neuromuscular control of movement. It follows that electromyographic techniques are essential for this purpose. Scientific literature reveals relevant practical usability of such information. This is the rationale for a review of the historical, physiological, technical and methodological background of electromyographic analysis of movement. The field of management and rehabilitation of motor disability is identified as one important application area. On the basis of relevant literature, the present paper asserts that scientific analysis of human movement patterns can materially affect patient treatment. It provides evidence that patient management and rehabilitation processes in central neurological disorders can be improved through electromyographic techniques. In particular, this evidence supports the use of electromyography for surgical planning in children with cerebral palsy. The paper concludes with a view on future directions in research, development and applications of scientific analysis of human movement. Copyright 1998 Elsevier Science B.V.

Entities:  

Year:  1998        PMID: 10200405     DOI: 10.1016/s0966-6362(98)00025-3

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  17 in total

Review 1.  Surface electromyogram signal modelling.

Authors:  K C McGill
Journal:  Med Biol Eng Comput       Date:  2004-07       Impact factor: 2.602

2.  Validation of an accelerometer for determination of muscle belly radial displacement.

Authors:  T Zagar; D Krizaj
Journal:  Med Biol Eng Comput       Date:  2005-01       Impact factor: 2.602

3.  Kinematic and electromyography analysis of submaximal differences running on a firm surface compared with soft, dry sand.

Authors:  Hugh C Pinnington; David G Lloyd; Thor F Besier; Brian Dawson
Journal:  Eur J Appl Physiol       Date:  2005-04-07       Impact factor: 3.078

4.  Evaluation of masseter muscle electromyography after surgical extraction of third molar.

Authors:  Chan-Woo Kim; Seong-Gon Kim; Sung-Wook Park; Young-Joon Chee
Journal:  Oral Maxillofac Surg       Date:  2014-02-13

Review 5.  A review of non-invasive techniques to detect and predict localised muscle fatigue.

Authors:  Mohamed R Al-Mulla; Francisco Sepulveda; Martin Colley
Journal:  Sensors (Basel)       Date:  2011-03-24       Impact factor: 3.576

6.  Techniques of EMG signal analysis: detection, processing, classification and applications.

Authors:  M B I Raez; M S Hussain; F Mohd-Yasin
Journal:  Biol Proced Online       Date:  2006-03-23       Impact factor: 3.244

Review 7.  The effectiveness of FES-evoked EMG potentials to assess muscle force and fatigue in individuals with spinal cord injury.

Authors:  Morufu Olusola Ibitoye; Eduardo H Estigoni; Nur Azah Hamzaid; Ahmad Khairi Abdul Wahab; Glen M Davis
Journal:  Sensors (Basel)       Date:  2014-07-14       Impact factor: 3.576

8.  EEG hyperscanning in motor rehabilitation: a position paper.

Authors:  Matthew R Short; Julio C Hernandez-Pavon; Alyssa Jones; Jose L Pons
Journal:  J Neuroeng Rehabil       Date:  2021-06-10       Impact factor: 4.262

9.  Novel pseudo-wavelet function for MMG signal extraction during dynamic fatiguing contractions.

Authors:  Mohammed Rashid Al-Mulla; Francisco Sepulveda
Journal:  Sensors (Basel)       Date:  2014-05-28       Impact factor: 3.576

10.  A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.

Authors:  Hendrik Wöhrle; Marc Tabie; Su Kyoung Kim; Frank Kirchner; Elsa Andrea Kirchner
Journal:  Sensors (Basel)       Date:  2017-07-03       Impact factor: 3.576

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