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

• URL: http://arxiv.org/abs/2206.14947v1
• Date: Wed, 29 Jun 2022 23:22:18 GMT
• Title: Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise
• Authors: Tekin Gunasar, Alexandra Rekesh, Atul Nair, Penelope King, Anastasiya Markova, Jiaqi Zhang, and Isabel Tate
• Abstract summary: We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. 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.
• Score: 51.76329821186873
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification [49.09505771145326]
  We propose a Hierarchical Dynamic Labeling (HDL) algorithm that does not depend on model predictions and utilizes image embeddings to generate sample labels. Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.
  arXiv  Detail & Related papers  (2024-04-26T06:00:27Z)
• One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers [57.441926088870325]
  Deep Image Prior (DIP) is a technique that optimized the weights of a randomly-d convolutional neural network to fit a signal from noisy or under-determined measurements. Relative to publicly available implementations of Vector Fitting (VF), our method shows superior performance on nearly all test examples.
  arXiv  Detail & Related papers  (2023-06-06T20:28:37Z)
• Anomaly Detection with Ensemble of Encoder and Decoder [2.8199078343161266]
  Anomaly detection in power grids aims to detect and discriminate anomalies caused by cyber attacks against the power system. We propose a novel anomaly detection method by modeling the data distribution of normal samples via multiple encoders and decoders. Experiment results on network intrusion and power system datasets demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-03-11T15:49:29Z)
• ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data [93.06336507035486]
  Generative Adversarial Networks (GANs) typically suffer from overfitting when limited training data is available. We present ScoreMix, a novel and scalable data augmentation approach for various image synthesis tasks.
  arXiv  Detail & Related papers  (2022-10-27T02:55:15Z)
• Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning [57.163525407022966]
  Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class. We propose AutoSMOTE, an automated over-sampling algorithm that can jointly optimize different levels of decisions.
  arXiv  Detail & Related papers  (2022-08-26T04:28:01Z)
• Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition [5.816418334578875]
  We propose a data augmentation strategy based on mixing signals for AMR of radio signals. Experiments show that our proposed method can improve the classification accuracy of deep learning based AMR models.
  arXiv  Detail & Related papers  (2022-04-05T07:40:16Z)
• IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance [1.854931308524932]
  Non-parametric data augmentation with Generative Adversarial Networks (GANs) offers a promising solution. We propose Imputation Balanced GAN (IB-GAN), a novel method that joins data augmentation and classification in a one-step process via an imputation-balancing approach.
  arXiv  Detail & Related papers  (2021-10-14T15:31:16Z)
• Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration Data [0.0]
  Time-series data are one of the fundamental types of raw data representation used in data-driven techniques. Deep Neural Networks (DNNs) require huge labeled training samples to reach their optimum performance. In this study, a data augmentation technique named ensemble augmentation is proposed to overcome this limitation.
  arXiv  Detail & Related papers  (2021-08-06T20:04:29Z)
• A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification [2.4727719996518487]
  This paper proposes a manifold learning framework to classify two types of EEG data from motor imagery (MI) tasks. For feature extraction, it is implemented by Common Spatial Pattern (CSP) from the preprocessed EEG signals. In the neighborhoods of the features for classification, the local approximation to the support of the data is obtained, and then the features are assigned to the classes with the closest support.
  arXiv  Detail & Related papers  (2021-07-30T06:18:05Z)
• Ensemble Wrapper Subsampling for Deep Modulation Classification [70.91089216571035]
  Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems.
  arXiv  Detail & Related papers  (2020-05-10T06:11:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.