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 < 0.05), though the QP model was slightly better. The normalized root mean square error (NRMSE) and goodness of fit R of the MS estimation by the QP model were: 0.1343 0.0296 and 0.8273 0.0376. There was no significant difference in the MS estimation results in different conditions (p < 0.05).
|Number of pages||8|
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 1 May 2020|
|Event||2020 5th International Conference on Intelligent Computing and Signal Processing, ICSP 2020 - Suzhou, China|
Duration: 20 Mar 2020 → 22 Mar 2020