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

TL;DR

This paper presents a decision forest-based approach for classifying EMG signals into six gestures using a small dataset augmented with Gaussian noise, emphasizing simple feature extraction methods suitable for online applications.

Contribution

It demonstrates that elementary feature extraction combined with data augmentation and ensemble models can achieve high accuracy in EMG gesture classification with limited data.

Findings

Random Forest and XGBoost achieved high accuracy.

Data augmentation with Gaussian noise improved model performance.

Elementary features outperformed more complex methods like Fourier Transform.

Abstract

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.