Performance Comparison of Different Classifiers Applied to Gesture Recognition from sEMG Signals

In the last years, surface electromyography (sEMG) has become a hot spot for research on signal classification methods due to its many applications with consumer grade sensors. Nonetheless, the correct classification of sEMG signals is not simple due to their stochastic nature and high variability. Our objective was to provide a comprehensive comparison between schemes used on the latest research, namely convolution neural networks (CNN) and hyperdimensional computing (HDC) using a public high-quality dataset. Our results showed that while CNN had substantially higher accuracy (68 vs. 32% for HDC, for 18 gestures), its shortcomings may be more prevalent in this area, as low amounts of training data, and lack of subject specific data can cause an accuracy drop of up to 70%/19% and 56%/7% for CNN/HDC, respectively. Our results point out that while HDC cannot reach CNN classification levels it is more versatile on small datasets and provides more adaptability.