Volltext-Downloads (blau) und Frontdoor-Views (grau)

Artificial Muscle Signal Generation using Generative Adversarial Networks

  • This work aims to generate synthetic electromyographic (EMG) signals using Generative Adversarial Network (GAN). GANs are considered as one of the most exciting and promising approaches in deep learning [6], offering the possibility to generate artificial data based on real data. GAN consists of two main parts, a discriminator that attempts to differentiate between the generated data and the original data, and a generator that tries to fool the discriminator by generating data which looks like real data, the GAN works by staging a two-player minimax game between generator and discriminator networks. To achieve the objective of generating realistic artificial electromyographic signals, two different architectures are considered for the generator and the discriminator networks of the GAN model: Long short-term memory (LSTM), which can avoid the long-term dependency problem and remembers information over a long period of time, and convolutional neural network (CNN), which is a powerful tool at automatic feature extraction. Different combinations of CNN and LSTM including hybrid model are experimented within the GAN using the same training data-set. The results and performances of each combination are compared and reviewed. The generated artificial EMG signals can be used to simulate real muscle activity situations to for example improve muscle signal controlled prostheses using artificial data that may include conditions that does not exist in real data. This method of artificial data generation is not limited to EMG signals, the network can also be used to generate other synthetic biomedical signals such as electroencephalogram (EEG) or electrocardiogram (ECG) that can be practically used for testing algorithms and classifiers.

Download full text files

Export metadata

Additional Services

Search Google Scholar


Author:Mahdi El Mesoudy
Document Type:Bachelor Thesis
Year of Completion:2021
Date of final exam:2022/01/26
Release Date:2022/05/04
Page Number:51
Institutes:Fachbereich 1 - Institut Informatik
DDC class:300 Sozialwissenschaften / 330 Wirtschaft
Licence (German):License LogoNo Creative Commons