An Investigation Into Time Domain Features of Surface Electromyography to Estimate the Elbow Joint Angle
Triwiyanto Triwiyanto, Oyas Wahyunggoro, Hanung Adi Nugroho, Herianto Herianto
DOI: 10.15598/aeee.v15i3.2177
Abstract
In literature, it is well established that feature extraction and pattern classification algorithms play essential roles in accurate estimation of the elbow joint angle. The problem with these algorithms, however, is that they require a learning stage to recognize the pattern as well as capture the variability associated with every subject when estimating the elbow joint angle. As EMG signals can be used to represent motion, we developed a non-pattern recognition method to estimate the elbow joint angle based on twelve timedomain features extracted from EMG signals recorded from bicep muscles alone. The extracted features were smoothed using a second order Butterworth low pass filter to produce the estimation. The accuracy of the estimated angles was evaluated by using the Pearson’s Correlation Coefficient (PCC) and Root Mean Square Error (RMSE).The regression parameters (Euclidean distance, R2 and slope) were then calculated to observe the effect of the features on elbow joint angle estimation. In this investigation, we found that for a 10- second long recording period, the MyoPulse Percentage (MYOP) Rate produced the best accuracy: with PCC of 0.97 ± 0.02 (Mean±SD) and RMSE of 11.37 ± 3.04◦ (Mean±SD), respectively. The MYOP feature also showed the highest R2 and slope value of 0.986±0.0083 (Mean’s) and 0.746 ± 0.17 (Mean’s), respectively for flexion and extension motions during all recorded periods.