Fig. 2

Overview of model training and evaluation scheme. A Random Forest (RF) classifier was trained to predict participants’ diagnoses based on kinematic features extracted from the IMUs. For each fold, the datasets were split into a training set (24 study participants) and a testing set (six study participants) such that both classes were evenly represented. Classification performance was assessed by calculating the AUROC score for the merged predictions on held-out test sets