Muscle fatigue is a severe problem for elite athletes, and this is due

to the long resting times, which can vary. Various mechanisms can

cause muscle fatigue which signifies that the specific muscle has

reached its maximum force and cannot continue the task. This thesis

was about surveying and exploring state-of-the-art methods and

systematically, theoretically, and practically testing the applicability

and performance of more recent machine learning methods on an existing

EMG to muscle fatigue pipeline. Several challenges within the

EMG domain exist, such as inadequate data, finding the most suitable

model, and how they should be addressed to achieve reliable

prediction. This required approaches for addressing these problems

by combining and comparing various state-of-the-art methodologies,

such as data augmentation techniques for upsampling, spectrogram

methods for signal processing, and transfer learning to gain a reliable

prediction by various pre-trained CNN models.

The approach during this study was to conduct seven experiments

consisting of a classification task that aims to predict muscle fatigue

in various stages. These stages are divided into 7 classes from 0-6, and

higher classes represent a fatigued muscle. In the tabular part of the

experiments, the Decision Tree, Random Forest, and Support Vector

Machine (SVM) were trained, and the accuracy was determined. A

similar approach was made for the spectrogram part, where the signals

were converted to spectrogram images, and with a combination

of traditional- and intelligent data augmentation techniques, such as

noise and DCGAN, the limited dataset was increased. A comparison

between the performance of AlexNet, VGG16, DenseNet, and InceptionV3

pre-trained CNN models was made to predict differences in

jump heights.

The result was evaluated by implementing baseline classifiers on

tabular data and pre-trained CNN model classifiers for CWT and

STFT spectrograms with and without data augmentation. The evaluation

of various state-of-the-art methodologies for a classification

problem showed that DenseNet and VGG16 gave a reliable accuracy

of 89.8 % on intelligent data augmented CWT images.

The intelligent data augmentation applied on CWT images allows

the pre-trained CNN models to learn features that can generalize unseen

data. Proving that the combination of state-of-the-art methods

can be introduced and address the challenges within the EMG domain.