A Dynamic Procedure to Detect Maximum Voluntary Contractions in Low Back

Author:

Wang Xun1,Beltran Martinez Karla1,Golabchi Ali23,Tavakoli Mahdi4ORCID,Rouhani Hossein1ORCID

Affiliation:

1. Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada

2. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada

3. EWI Works International Inc., Edmonton, AB T6E 3N8, Canada

4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada

Abstract

Surface electromyography (sEMG) is generally used to measure muscles’ activity. The sEMG signal can be affected using several factors and vary among individuals and even measurement trials. Thus, to consistently evaluate data among individuals and trials, the maximum voluntary contraction (MVC) value is usually calculated and used to normalize sEMG signals. However, the sEMG amplitude collected from low back muscles can be frequently larger than that found when conventional MVC measurement procedures are used. To address this limitation, in this study, we proposed a new dynamic MVC measurement procedure for low back muscles. Inspired by weightlifting, we designed a detailed dynamic MVC procedure, and then collected data from 10 able-bodied participants and compared their performances using several conventional MVC procedures by normalizing the sEMG amplitude for the same test. The sEMG amplitude normalized by our dynamic MVC procedure showed a much lower value than those obtained using other procedures (Wilcoxon signed-rank test, with p < 0.05), indicating that the sEMG collected during dynamic MVC procedure had a larger amplitude than those of conventional MVC procedures. Therefore, our proposed dynamic MVC obtained sEMG amplitudes closer to its physiological maximum value and is thus more capable of normalizing the sEMG amplitude for low back muscles.

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference25 articles.

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3. Naik, G.R. (2012). Computational Intelligence in Electromyography Analysis, IntechOpen.

4. Evaluation of recommended maximum voluntary contraction exercises for back muscles commonly investigated in ergonomics;Saba;Theor. Issues Ergon. Sci.,2020

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