Smoothed arg max Extreme Learning Machine: An Alternative to Avoid Classification Ripple in sEMG Signals

Despite all the recent developments of using the surface electromyography (sEMG) as a control signal, reliable classifications still remain an arduous task due to overlapping classes and classification ripples. In this paper, we present a straightforward approach to avoid classification ripple based on smoothing the arg max value of an Extreme Learning Machine (ELM) classifier. We compare the baseline accuracy of the classifier with an arg max filtered by a traditional Exponential Smoothing Filter (ESF) and our adaptation of Antonyan Vardan Transform (AVT). The classifiers were evaluated using sEMG data acquired through 12 channels from four subjects performing 17 different movements of forearm and fingers with three repetitions each. In the best scenario, our methods reached results higher than 96% and 82% of overall and weighted accuracy, respectively. Those results match or outperform similar papers of the literature using a simpler model, which may help the application of the techniques on embedded platforms and make the practical use of such devices more feasible.