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在疲劳收缩中使用时变表面肌电图特征进行关节力估计。

Joint force estimation using time-varying SEMG feature in fatiguing contraction.

作者信息

Na Youngjin, Kim Yunjoo, Kim Jung

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3586-9. doi: 10.1109/EMBC.2014.6944398.

Abstract

Many studies have estimated joint force using surface electromyography (SEMG), however, the time-variant characteristic of SEMG is not considered. The change of SEMG amplitude is one of manifestations of muscle fatigue. This study proposes a force estimation method using SEMG in fatiguing contraction. The SEMG amplitude is used to determine the signal states by k-means clustering method. According to the signal state changes, the corresponding gain is used to estimate the force. The target contraction is an isometric abduction of an index finger in static and dynamic force conditions for 5 healthy subjects. The estimation performance was evaluated by percentage of root mean squared error (RMSE). The RMSE for the proposed method is 2.5 ± 1.0% under static condition and 8.8 ± 1.2% under dynamic condition. The accuracy using a constant gain calculated at initial time was used to compare with the proposed method. The RMSE are 8.9 ± 2.2% under static condition and 10.1 ± 2.4% under dynamic condition. The proposed method had better performance in both conditions.

摘要

许多研究使用表面肌电图(SEMG)来估计关节力,然而,并未考虑SEMG的时变特性。SEMG幅度的变化是肌肉疲劳的表现之一。本研究提出了一种在疲劳收缩中使用SEMG的力估计方法。通过k均值聚类方法利用SEMG幅度来确定信号状态。根据信号状态变化,使用相应增益来估计力。目标收缩是5名健康受试者在静态和动态力条件下食指的等长外展。通过均方根误差(RMSE)百分比来评估估计性能。所提方法在静态条件下的RMSE为2.5±1.0%,在动态条件下为8.8±1.2%。将使用初始时刻计算的恒定增益的准确性与所提方法进行比较。在静态条件下RMSE为8.9±2.2%,在动态条件下为10.1±2.4%。所提方法在两种条件下均具有更好的性能。

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