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Electromyography signals can be used as training data by machine learningmodels to classify various gestures. We seek to produce a model that canclassify six different hand gestures with a limited number of samples thatgeneralizes well to a wider audience while comparing the effect of our featureextraction results on model accuracy to other more conventional methods such asthe use of AR parameters on a sliding window across the channels of a signal.We appeal to a set of more elementary methods such as the use of random boundson a signal, but desire to show the power these methods can carry in an onlinesetting where EMG classification is being conducted, as opposed to morecomplicated methods such as the use of the Fourier Transform. To augment ourlimited training data, we used a standard technique, known as jitter, whererandom noise is added to each observation in a channel wise manner. Once alldatasets were produced using the above methods, we performed a grid search withRandom Forest and XGBoost to ultimately create a high accuracy model. For humancomputer interface purposes, high accuracy classification of EMG signals is ofparticular importance to their functioning and given the difficulty and cost ofamassing any sort of biomedical data in a high volume, it is valuable to havetechniques that can work with a low amount of high-quality samples with lessexpensive feature extraction methods that can reliably be carried out in anonline application.

Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise