## 2 Methods
### Statistical Analysis
#### 2.5.2 Primary Analyses  
* Classification of participants  
Participants were classified based on the median scores of both energy (vigor) and fatigue scores on the POM-SF. Chi-squares were used to identify differences in sex distribution between groups and student T-tests were used to identify differences in age, height, weight and Body Mass Index (BMI) between each group (2 groups) An ANOVA was used to identify differences for age, height, weight and BMI between the groups (4 groups) that were split by their energy and fatigue status. _Objective 1:_  
Participants who reported a score > 5 (median value) on the energy (vigor) portion of the POMS were classified as \"high energy\", while individuals who scores \(\leq\) 5 were classified as \"low energy\". _Objective 2:_  
For fatigue, participants who reported a score > 3 (median value) on the fatigue portion of the POMS-SF were classified as \"high fatigue\", while participants who reported scores \(\leq\) 3 were classified as \"low fatigue\". _Objective 3:_  
Using the sub-categories from Objectives 1 and 2, participants were classified into the following groups: high energy and high fatigue, high energy and low fatigue, low energy and high fatigue, and low energy and low fatigue. For each objective, the following analyses were conducted:  
_Sub-Objective 1: Important Feature Selection_  
When recording high-dimensional features, not every feature is equally important, and there may be many redundant features that are of less importance. Therefore, to optimize the number of features for each classification model, a Recursive Feature Elimination with cross-validation model using Random Forest Classifier as an estimator were used. Furthermore, selection from model or forward Sequential Feature Selector were used to extract the dataset with the 12 most important features to optimize accuracy of the models [49]. _Sub-Objective 2: Model Training and Evaluation_After sorting the features, the dataset was used to train the model through both Classifiers and Regressors respectively. For the Classifiers, the categories for energy, fatigue and energy + fatigue were used to train each of the models. Models used for classification were Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, K Nearest Neighbour (KNN) Classifier, Multi-Layer Perceptron (MLP) Classifier, Support Vector Classifier (SVC) Model, Gaussian Naive Bayes Model, and Bagging Classifier. Both moods were treated as continuous variables for the Regressor models. Models used for regressors were: Decision Tree Regressor, Random Forest Regressor, Ridge Regression, Lasso Regression, Stochastic Gradient Descent (SGD) Regression MLP Regressor, Support Vector Regressor (SVR) Model, Gradient Boosting Regressor (GBR) Model, Neighbors Regressor, and Elastic Net Model. Each model was trained in a 10-fold cross-validation manner in order to avoid problems such as overfitting or selection bias to some degree [50]. Regressor models were also assessed using bootstrapping, as this allows validation of models with small sample sizes [51]. The models were randomly split into the training set (90%) and the test set (10%). Classification models were evaluated based on mean and median \"Accuracy\" and the area under the curve (AUC) of Receiver Characteristic Operator (ROC), while Mean Absolute Error (MAE) was used to assess Regressor accuracy. Correlation coefficients (R\({}^{2}\)) were also assessed between the predicted mood score and the self-reported mood score on the regressor models [44]. _Sub-Objective 3: Differences in gait characteristics between groups_  
To identify differences between groups (low vs. high energy; low vs. high fatigue; high energy/high fatigue vs. high energy/low fatigue vs. low energy/high fatigue vs. low energy/low fatigue), Multivariate Analysis of Covariance (MANCOVA) were used with sex, age, weight, and height used as co-variates.