Characterization of individual muscle activities during isometric contraction using ultrasound imaging

Jizhou Li, Yongjin Zhou, Xin Chen, Guo Dan, Yongping Zheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

The quantification of human muscle contraction is required by many biomechanical studies. Ultrasound imaging has been widely used to study muscle function for decades, and morphological parameters, such as muscle fiber length, muscle thickness, pennation angle and cross sectional area, have been used to reflect muscle contraction. However, few details regarding the motion activity of a muscle group (such as quadriceps muscle, QM) or an individual muscle (such as rectus femoris, RF), during its working for the completion of a movement, were quantitatively investigated. This study introduces a novel approach based on motion feature extraction to provide a new descriptor for local muscle activities during the completion of pre-defined body movements. Specifically speaking, this approach is applied to investigate the QM during isometric contraction in eight subjects using ultrasound imaging. Preliminary results indicate that the proposed approach is capable of providing new perspectives about the activity patterns of both RF and QM.
Original languageEnglish
Title of host publication2014 19th International Conference on Digital Signal Processing, DSP 2014
PublisherIEEE
Pages162-166
Number of pages5
Volume2014-January
ISBN (Electronic)9781479946129
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 19th International Conference on Digital Signal Processing, DSP 2014 - Hong Kong, Hong Kong
Duration: 20 Aug 201423 Aug 2014

Conference

Conference2014 19th International Conference on Digital Signal Processing, DSP 2014
Country/TerritoryHong Kong
CityHong Kong
Period20/08/1423/08/14

Keywords

  • Individual muscle activity
  • Isometric contraction
  • Ultrasonography

ASJC Scopus subject areas

  • Signal Processing

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