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.