The sensitivity of autoregressive model coefficient in quantification of trunk muscle fatigue during a sustained isometric contraction

Abstract

The goal of the study is to investigate the performance of the first autoregressive model coefficient (ARC) in quantification of fatigue of the trunk muscle. Other parameters such as root mean square (RMS), zero crossing rate (ZCR), mean power frequency (MPF), median frequency (MF) were computed and used for comparison with ARC in this study using slope and coefficient of determination of linear regression model fitted to the normalized parameter values in the time domain in order to assess sensitivity and reliability. Ten males were utilized and electromyographic (EMG) signals were collected at L3/L4 levels of both right and left erector spinae muscles continuously for a period of 20 s while subjects were isometrically extending their trunk at five different force levels: 15%, 30%, 45%, 60%, and 75% of maximal voluntary contraction (MVC). RMS was found to be the worst parameter, ARC was the most sensitive at 15–45% MVC and reliable at 15–60% MVC, and ZCR was more sensitive at 60–75% MVC but less reliable. The fourth order of the autoregressive (AR) model in 0.5 s intervals was validated for EMG signals and ARC may become an potential parameter to describe the trunk fatigue during both static and dynamic exertions. Relevance to industry The first coefficient of autoregressive model can be used as an potential measure to assess the muscle fatigue caused by a prolonged static working posture. Its relatively sensitive fatigue-detecting nature may assist ergonomists in preventing workers from injuries due to cumulative muscle fatigue even at relatively lower force levels.