...
首页> 外文期刊>IEEE transactions on neural systems and rehabilitation engineering >Dynamic Elbow Flexion Force Estimation Through a Muscle Twitch Model and sEMG in a Fatigue Condition
【24h】

Dynamic Elbow Flexion Force Estimation Through a Muscle Twitch Model and sEMG in a Fatigue Condition

机译:通过肌肉抽搐模型和sEMG在疲劳条件下的动态肘部弯曲力估算

获取原文
获取原文并翻译 | 示例
           

摘要

We propose a joint force estimation method to compute elbow flexion force using surface electromyogram (sEMG) considering time-varying effects in a fatigue condition. Muscle fatigue is a major cause inducing sEMG changes with respect to time over long periods and repetitive contractions. The proposed method composed the muscle-twitch model representing the force generated by a single spike and the spikes extracted from sEMG. In this study, isometric contractions at six different joint angles (10 subjects) and dynamic contractions with constant velocity (six subjects) were performed under non-fatigue and fatigue conditions. Performance of the proposed method was evaluated and compared with that of previous methods using mean absolute value (MAV). The proposed method achieved average 6.7 ± 2.8 %RMSE for isometric contraction and 15.6 ± 24.7%RMSE for isokinetic contraction under fatigue condition with more accurate results than the previous methods.
机译:考虑到疲劳条件下的时变效应,我们提出了一种联合力估计方法来使用表面肌电图(sEMG)计算肘部屈曲力。肌肉疲劳是导致sEMG长时间随时间变化和反复收缩的主要原因。所提出的方法由代表单个尖峰产生的力和从sEMG提取的尖峰的肌肉抽搐模型组成。在这项研究中,在非疲劳和疲劳条件下进行了六个不同关节角度的等距收缩(10个对象)和等速动态收缩(六个对象)。评价了所提出方法的性能,并与使用平均绝对值(MAV)的先前方法进行了比较。所提出的方法在疲劳条件下,等距收缩平均达到6.7±2.8%RMSE,等速收缩达到15.6±24.7%RMSE,其结果比以前的方法更为准确。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号