Assessment of Waveform Similarity in Electromyographical Clinical Gait Data: The Linear Fit Method

Marco Iosa, Antonella Peppe, Giovanni Morone, Sonia Bottino, Fabiano Bini, Franco Marinozzi, Stefano Paolucci

Research output: Contribution to journalArticlepeer-review

Abstract

The assessment of waveform similarity is a crucial issue in gait analysis for the comparison of electromyography (EMG) and kinematic patterns with reference data. A typical scenario is in fact the comparison of a patient’s EMG pattern with a relevant physiological pattern. Many methods have been proposed for a quantitative comparison of the two patterns, suggesting the absence of a gold standard. A recently proposed method for comparing kinematic patterns is the linear fit method (LFM). This study aims at testing the applicability of this method on data of EMG. The validity of LFM was tested in terms of appropriateness, sensitivity, specificity, and reliability, by comparing 20 EMG pathological gait patterns (obtained by a group of patients with Parkinson’s Disease) and 20 EMG physiological gait patterns (obtained by healthy subjects). When gastrocnemious and tibialis anterior EMG activity was analyzed, the appropriateness of LFM in discriminating pathological patterns resulted of 97.5%, with a sensitivity of 95% and a specificity of 100%. The reliability was good for 2 out of 3 parameters in each group of subjects. The LFM resulted a simple method suitable for analysing the waveform similarity in gait EMG clinical analysis.

Original languageEnglish
Pages (from-to)774-781
Number of pages8
JournalJournal of Medical and Biological Engineering
Volume38
Issue number5
DOIs
Publication statusPublished - Oct 1 2018

Keywords

  • Electromyography (EMG)
  • Gait analysis
  • Muscle activity
  • Parkinson’s Disease
  • Rehabilitation

ASJC Scopus subject areas

  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Assessment of Waveform Similarity in Electromyographical Clinical Gait Data: The Linear Fit Method'. Together they form a unique fingerprint.

Cite this