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

06/29/2022
by   Tekin Gunasar, et al.
3

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.

READ FULL TEXT

page 2

page 4

page 8

research
03/16/2020

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Jamming attacks target a wireless network creating an unwanted denial of...
research
10/21/2022

Feature Engineering and Classification Models for Partial Discharge in Power Transformers

To ensure reliability, power transformers are monitored for partial disc...
research
10/23/2019

Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning

We present a Trojan (backdoor or trapdoor) attack that targets deep lear...
research
10/07/2020

Interpreting Imagined Speech Waves with Machine Learning techniques

This work explores the possibility of decoding Imagined Speech (IS) sign...
research
12/30/2021

Feature extraction with mel scale separation method on noise audio recordings

This paper focuses on improving the accuracy of noise audio recordings. ...
research
01/02/2019

Adaptive EMG-based hand gesture recognition using hyperdimensional computing

Accurate recognition of hand gestures is crucial to the functionality of...
research
07/31/2021

A Plant Root System Algorithm Based on Swarm Intelligence for One-dimensional Biomedical Signal Feature Engineering

To date, very few biomedical signals have transitioned from research app...

Please sign up or login with your details

Forgot password? Click here to reset