首页> 外文会议>2007 IEEE/ICME INTERNATIONAL CONFERENCE ON COMPLEX MEDICAL ENGINEERING >Handgrip Force Estimation Based on A Method Using Surface Electromyography (sEMG) of Extensor Carpi Radialis Longus
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Handgrip Force Estimation Based on A Method Using Surface Electromyography (sEMG) of Extensor Carpi Radialis Longus

机译:基于伸肌腕(肌表面肌电图(sEMG)方法的手握力估计

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Both flexor and extensor muscle activated together when hand-grip task conducted, but there is little work that attempts to specifically investigate the relationship of the hand-grip force level and the EMG activity of extensor muscles. The present study was designed to investigate the correlation between hand-grip force level and sEMG of ECRL (extensor carpi radialis longus, ECRL). A pseudo-randomized sequence of hand-grip tasks with some specific force ranges has been defined for calibration. Eight subjects (university students, five males and three females) were recruited to conduct both calibration trials and voluntary trials. EMG signals have been preprocessed with RMS (Root-Mean-Square) method, after which EMG signals are normalized with amplitude value of MVC-related EMG. With data regression of calibration trials, a linear model has been developed to correlate the handgrip force output with sEMG activities of ECRL and this linear model then is employed to estimate the hand-grip force production of voluntary trials. The Root-Mean-Square-Error (RMSE) of the estimated force output for all the voluntary trials are statistically compared in different force ranges. The results indicate that the linear model is useful to estimate the handgrip force based on the EMG activities of forearm extensor muscle, and the accuracy of this model is dependent on the force levels. That is the linear model can provide best estimation in moderate force range (30%-50%MVC), while the force prediction error tends to be large for weak force (20%-30%MVC) or strong force (50%-80%MVC).
机译:当执行手握任务时,屈肌和伸肌同时激活,但是很少有工作试图专门研究手握力水平与伸肌肌电活动的关系。本研究旨在研究手握力水平与ECRL(radial腕腕长肌,ECRL)的sEMG之间的相关性。已定义了具有某些特定作用力范围的伪握任务的伪随机序列以进行校准。招募了八名受试者(大学学生,五名男性和三名女性)进行校准试验和自愿试验。已使用RMS(均方根)方法对EMG信号进行了预处理,然后使用MVC相关EMG的幅度值对EMG信号进行归一化。通过校准试验的数据回归,开发了线性模型,以将手握力输出与ECRL的sEMG活动相关联,然后将该线性模型用于估算自愿试验的手握力产生量。所有自愿试验的估计力输出的均方根误差(RMSE)在不同的力范围内进行统计比较。结果表明,线性模型可用于根据前臂伸肌的肌电图活动估计手握力,该模型的准确性取决于力度。也就是说,线性模型可以在中等作用力范围(30%-50%MVC)中提供最佳估计,而对于弱作用力(20%-30%MVC)或强作用力(50%-80 %MVC)。

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