Na Youngjin, Kim Sangjoon J, Kim Jung
Department of Mechanical Engineering, The University of Texas at Austin, TX, USA.
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Med Eng Phys. 2017 Dec;50:103-108. doi: 10.1016/j.medengphy.2017.10.002. Epub 2017 Oct 18.
We propose a force estimation method in fatigue condition using a muscle-twitch model and surface electromyography (sEMG). The twitch model, which is an estimate of force by a single spike, was obtained from sEMG features and measured forces. Nine healthy subjects performed isometric index finger abduction until exhaustion for a series of dynamic contractions (0-20% MVC) to characterize the twitch model and static contractions (50% MVC) to induce muscle fatigue. Muscle fatigue was identified based on the changes of twitch model; the twitch peak decreased and the contraction time increased as muscle fatigue developed. Force estimation performance in non-fatigue and fatigue conditions was evaluated and its results were compared with that of a conventional method using the mean absolute value (MAV). In non-fatigue conditions, the performance of the proposed method (0.90 ± 0.05) and the MAV method (0.88 ± 0.06) were comparable. In fatigue conditions, the performance was significantly improved for the proposed method (0.87 ± 0.05) compared with the MAV (0.78 ± 0.09).
我们提出了一种在疲劳状态下使用肌肉抽搐模型和表面肌电图(sEMG)进行力估计的方法。通过sEMG特征和测量的力获得了抽搐模型,该模型是对单个峰值产生的力的估计。九名健康受试者进行了等长食指外展,直至疲劳,包括一系列动态收缩(0-20%最大自主收缩,MVC)以表征抽搐模型,以及静态收缩(50%MVC)以诱导肌肉疲劳。基于抽搐模型的变化识别肌肉疲劳;随着肌肉疲劳的发展,抽搐峰值下降,收缩时间增加。评估了非疲劳和疲劳状态下的力估计性能,并将其结果与使用平均绝对值(MAV)的传统方法进行了比较。在非疲劳状态下,所提出方法的性能(0.90±0.05)和MAV方法的性能(0.88±0.06)相当。在疲劳状态下,与MAV(0.78±0.09)相比,所提出方法的性能显著提高(0.87±0.05)。