Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise
Electromyography signals can be used as training data by machine learning
models to classify various gestures. We seek to produce a model that can
classify six different hand gestures with a limited number of samples that
generalizes well to a wider audience while comparing the effect of our feature
extraction results on model accuracy to other more conventional methods such as
the 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 bounds
on a signal, but desire to show the power these methods can carry in an online
setting where EMG classification is being conducted, as opposed to more
complicated methods such as the use of the Fourier Transform. To augment our
limited training data, we used a standard technique, known as jitter, where
random noise is added to each observation in a channel wise manner. Once all
datasets were produced using the above methods, we performed a grid search with
Random Forest and XGBoost to ultimately create a high accuracy model. For human
computer interface purposes, high accuracy classification of EMG signals is of
particular importance to their functioning and given the difficulty and cost of
amassing any sort of biomedical data in a high volume, it is valuable to have
techniques that can work with a low amount of high-quality samples with less
expensive feature extraction methods that can reliably be carried out in an
online application.