Relationship between muscle hardness estimated by the indentation method and muscle contractile level

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)

Abstract

The purpose of this study was to investigate the relationship between muscle contractile level and hardness by the indentation method. Eleven healthy male subjects were involved in this study. The subjects put their arms horizontally on a table. The subjects were instructed to keep the isometric contractile force constant at three elbow angles. The indentation depth and reaction force of the biceps brachii muscle were measured, and electromyogram (EMG) were done. First, EMGs before and during the indentation were compared. The EMGs showed no significant increase during the indentation. The indentation did not affect muscle activities. Next, the force indentation depth curves were approximated with the non-linear Voigt model by the least square method, and the indices of the elasticity and the viscosity were estimated. Then the relationship between the indices and the contractile levels were investigated. The contractile level was the force normalized by that in the maximum voluntary contraction. The elastic indices increased almost linearly as the contractile levels increased. The relationships at different elbow angles did not show significant differences. However, the characteristics of the viscous indices varied depending on the subjects.

Original languageEnglish
Pages (from-to)2290-2291
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 III
Publication statusPublished - 2004 Dec 1
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: 2004 Sept 12004 Sept 5

Keywords

  • Hardness
  • Indentation
  • Muscle
  • Upper arm

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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