We utilized Principal Component Analysis (PCA) and Time-lagged Independent Component Analysis (tICA) as dimensionality reduction techniques to identify coordinates that separate the states. The two unsupervised methods complement each other in regards to addressing the variances present in the dataset. For supervised dimensionality reduction, we opted for Linear Discriminant Analysis (LDA) due to its efficacy in separating distinct classes within a given dataset. To further enhance the separation capability of PCA and LDA, several methods of automated feature selection were chosen and implemented, namely Recursive Feature Elimination with Cross Validation 88 (RFECV) using both Random Forest (RF) and Logistic Regression (LR) as classifier models, and Lasso 91 with Cross Validation (LassoCV), all using fivefold stratified cross-validation. The RF and Logistic Regression classifiers were additionally evaluated by selecting only the top 10% of features. When subjecting the highlighted features to dimensionality reduction using PCA or LDA, we observed varying importance of the features to the principal components (Tables ). To alleviate this discrepancy between the results and increase separation performance, we defined a consensus model, which combines the results of all previously mentioned approaches and highlights features that are consistently found across all models. These features were identified by performing a PCA and LDA on each of the models and examining the loadings. Subsequently, we computed the average contributions to the first three principal components, as well as the two LDA components. The selected features needed to appear in at least 75 % of the elimination models to be deemed significant. This process yielded a set of 49 internal coordinates for the PCA. These coordinates were hierarchically clustered to group together correlated movements and then subjected to causal inference analysis (see Statistical Model for Causality Inference). In tandem with automated feature reduction methodologies, we leveraged Decision Trees to quantify the most relevant features from our dataset. These Decision Trees underwent training with class balancing. Furthermore, we adopted a Decision Rule framework, employing the skoperules library. Within this framework, a Random Forest bagging classifier, consisting of 30 Decision Trees, was used to provide a semantically quantitative characterization of the three ensembles.