文献类型：期刊文献

英文题名：Estimation of triceps muscle strength based on Mechanomyography

作者：Xie, Jiazhi[1]; Zhang, Yue[1]; Yang, Ke[1,2]; Xia, Chunming[1,3]

机构：[1] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] School of Science and Engineering, University of Dundee, Dundee, DD14HN, United Kingdom; [3] School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

年份：2020

卷号：1544

期号：1

外文期刊名：Journal of Physics: Conference Series

收录：EI(收录号：20202508834770)

语种：英文

外文关键词：Frequency estimation - Mean square error

摘要：The aim of this study is to establish a reliable and widely applicable muscle strength (MS) estimation model based on the Mechanomyography (MMG). Seven healthy male volunteers were recruited to collect MMG and MS during the isometric contraction of their triceps. For MMG, 18 features were extracted. For the extreme gradient boosting (XGBoost) model and the quadratic polynomial (QP) model, the feature combination with the best estimation result was selected. The MS estimation performance of the XGBoost model and the QP model were compared. The performance of the QP model on the estimation of MS in different frequencies, different fatigue states and time periods was evaluated by using t-test. The results showed that when the number of features exceeds three, the model estimation accuracy has not improved significantly; and there was no significant difference in the estimation result of MS between the two models (p ? 2019 Published under licence by IOP Publishing Ltd.