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Muscular Co-contraction Detection: A Wavelet Coherence Approach

  • Annachiara Strazza
  • Federica Verdini
  • Andrea Tigrini
  • Stefano Cardarelli
  • Alessandro Mengarelli
  • Sandro Fioretti
  • Francesco Di NardoEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)

Abstract

Muscular co-contraction is defined as the activity of agonist and antagonist muscles around a joint, enhancing joint stability and balance. The quantitative assessment of muscle co-contractions would be meaningful for deepening the comprehension of this physiological mechanism. Thus, the purpose of this work is to quantify muscle co-contraction using a Wavelet transform-based coherence analysis of sEMG signal during straight walking. To this purpose, sEMG from tibialis anterior (TA) and gastrocnemius lateralis (GL) and basographic signals were acquired in five healthy subjects during walking. Basographic signals were analyzed to quantify foot-floor contact. sEMG signals were processed using Wavelet Transform Coherence (WTC) to identify muscular co-contractions. Daubechies (order 4 with 6 levels of decomposition) was chosen as mother wavelet. A denoising algorithm based on a soft thresholding was applied for removing noise from raw signals. Denoised signals were considered to achieve WTC function, a well-known localized statistical assessment of cross-correlation between signals. Thus, in this work, WTC cross-correlation could be considered to assess muscular co-contraction. This methodology applied to TA and GL signals was able to detect GL/TA co-contractions during hell-strike (0–10% of GC) phase and during P-phase (54.2–68.3% of GC), matching with literature. Moreover, WTC approach was able to provide also the frequency band of information content for muscle co-contractions: 32–65 Hz for H-phase co-contraction and 16–32 Hz for P-phase co-contraction. In conclusion, this study proposed WTC analysis as a reliable method to assess muscle co-contraction in time-frequency domain.

Keywords

Muscle co-contraction Time-frequency analysis Wavelet coherence Surface EMG 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Annachiara Strazza
    • 1
  • Federica Verdini
    • 1
  • Andrea Tigrini
    • 1
  • Stefano Cardarelli
    • 1
  • Alessandro Mengarelli
    • 1
  • Sandro Fioretti
    • 1
  • Francesco Di Nardo
    • 1
    Email author
  1. 1.Department of Information EngineeringUniversità Politecnica delle MarcheAnconaItaly

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