Muscle synergies are a popular tool for assessing the modular organization of the central nervous system during several motor tasks. However, extracting muscle synergies has encountered issues such as the ability to compare their results when different sEMG signal pre-processing steps are applied or when using various methods to establish the number of them. The aim of this thesis is to evaluate the motor control strategies during walking through the use of graph theory and machine learning in order to overcome the method- ological limitations inherent in the theory of muscle synergies. The method is tested to quantitatively assess changes in muscle coordination behaviour between a group of 20 healthy subjects and a group of 20 Parkinson’s Disease patients.