Analysis of Survival Probability and Its Association with Time to Task Failure in Induced Fatiguing Dynamic Contractions of Biceps Brachii Muscle using Surface Electromyography
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引用次数: 0
Abstract
In this study, an attempt has been made to model the survival probability of muscle to predict time to task failure (TTF) during dynamic fatiguing contractions. For this purpose, seventy-three subjects are recruited to perform dynamic elbow flexion task with 3 kg and 6 kg-load dumbbells. Surface electromyography (sEMG) signals are recorded from the belly of biceps brachii muscle and time to task failure is noted. Survival analysis is performed using two probabilistic models, namely Weibull and Log-logistics, and Akaike Information Criteria (AIC) is utilized for model comparison. The results show that both types of distributions can be used to model the survival probability of muscle during fatiguing task. The AIC values are comparable between models, but it is found to be higher in case of loading with 6 kg. For twice the loading levels, the ratio of the scale parameters of distribution models are found to be 2.80 and 2.43 respectively, which is comparable with the ratio of 2.60 for the median RMS value in the first curl. The results demonstrate that the approach to model survival probability of muscle can aid in prediction of TTF during fatiguing tasks. This study could be clinically relevant in domains of sports medicine and applied ergonomics.
在这项研究中,试图建立肌肉存活概率模型,以预测动态疲劳收缩期间的任务失败时间(TTF)。为此,我们招募了73名受试者,用3公斤和6公斤负荷的哑铃执行动态屈肘任务。从肱二头肌腹部记录表面肌电图(sEMG)信号,并记录任务失败的时间。生存分析采用Weibull和Log-logistics两种概率模型,并采用Akaike Information Criteria (AIC)进行模型比较。结果表明,这两种分布都可以用来模拟疲劳任务时肌肉的生存概率。AIC值在不同型号之间具有可比性,但发现在负载为6 kg的情况下AIC值更高。在两种荷载水平下,分布模型尺度参数的比值分别为2.80和2.43,与第一次旋度中位均方根值的比值为2.60相当。结果表明,建立肌肉存活概率模型的方法可以帮助预测疲劳任务时的TTF。本研究在运动医学和应用人体工程学领域具有临床应用价值。