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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 time-domain 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, R^2 and slope) were calculated to observe the response of the features to the elbow-joint angle. From the investigation, we found, in the period of motion 10s, MYOP features have the best accuracy: 0.97±0.02 (Mean±SD) and 11.37±3.04˚ (Mean±SD) for correlation coefficient and RMSE respectively. MYOP features also showed the highest R^2 and slope value 0.986±0.0083 (Mean±SD) and 0.746±0.17 (Mean±SD) respectively for flexion and extension motion and all periods of motion.

An Investigation Into Time Domain Features of Surface Electromyography to Estimate the Elbow Joint Angle