Performance of Forearm FMG and sEMG for Estimating Elbow, Forearm and Wrist Positions  

作　　者：Zhen Gang Xiao Carlo Menon 

机构地区：[1]Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, British Columbia V3T0A3, Canada

出　　处：《Journal of Bionic Engineering》2017年第2期284-295,共12页仿生工程学报（英文版）

摘　　要：The ability to track upper extremity movement during activity of daily living has the potential to facilitate the recovery of individuals with neurological or physical injuries. Hence, the use of Surface Electromyography （sEMG） signals to predict upper extremity movement is an area of interest in the research community. A less established technique, Force Myography （FMG）, which uses force sensors to detect forearm muscle contraction patterns, is also able to detect some movements of the arm. This paper investigates the comparative performance of sEMG and FMG when predicting wrist, forearm and elbow positions using signals extracted from the forearm only. Support Vector Machine （SVM） and Linear Discriminant Analysis （LDA） classifiers were used to evaluate the prediction performance of both FMG and sEMG data. Ten healthy volunteers participated in this study. Under a cross validation across a repetition evaluation scheme, the SVM classifier obtained averaged accuracies of 84.3%, 82.4% and 71.0%, respectively, for predicting elbow, forearm and wrist positions using FMG; while sEMG yielded 75.4%, 83.4% and 92.4% accuracies for predicting the same respective positions. The accuracies obtained using SVM are slightly, but statistical significantly, higher than the ones obtained using LDA. However, the trends on the classification performances between FMG and sEMG are consistent. These results also indicate that the forearm FMG pattern is highly influenced by the change of elbow position, while the forearm sEMG is less subjected to the change. Overall, both forearm FMG and sEMG techniques provide abundant information that can be utilized for tracking the upper extremity movements.The ability to track upper extremity movement during activity of daily living has the potential to facilitate the recovery of individuals with neurological or physical injuries. Hence, the use of Surface Electromyography （sEMG） signals to predict upper extremity movement is an area of interest in the research community. A less established technique, Force Myography （FMG）, which uses force sensors to detect forearm muscle contraction patterns, is also able to detect some movements of the arm. This paper investigates the comparative performance of sEMG and FMG when predicting wrist, forearm and elbow positions using signals extracted from the forearm only. Support Vector Machine （SVM） and Linear Discriminant Analysis （LDA） classifiers were used to evaluate the prediction performance of both FMG and sEMG data. Ten healthy volunteers participated in this study. Under a cross validation across a repetition evaluation scheme, the SVM classifier obtained averaged accuracies of 84.3%, 82.4% and 71.0%, respectively, for predicting elbow, forearm and wrist positions using FMG; while sEMG yielded 75.4%, 83.4% and 92.4% accuracies for predicting the same respective positions. The accuracies obtained using SVM are slightly, but statistical significantly, higher than the ones obtained using LDA. However, the trends on the classification performances between FMG and sEMG are consistent. These results also indicate that the forearm FMG pattern is highly influenced by the change of elbow position, while the forearm sEMG is less subjected to the change. Overall, both forearm FMG and sEMG techniques provide abundant information that can be utilized for tracking the upper extremity movements.

关 键 词：Surface Electromyography （sEMG） Force Myography （FMG） classification limb movement activity monitoring upper extremity 

分 类 号：TP399[自动化与计算机技术—计算机应用技术] TN911.7[自动化与计算机技术—计算机科学与技术]