## 3 Evaluation of the Orientation Impact Model and HAR Performance  
A five-fold cross-validation [30] was used to train both the baseline and the augmented models. The cross-validation performance was used to determine the augmentation approach (i.e., the range of rotations), and the effect of the rotation on the baseline model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Train/Test** & **Participants** & **Rotations** & **Rotation Axis** & **Sensors’ Location** \\ \hline \begin{tabular}{c} Train \\ baseline \\ model \\ \end{tabular} & WPS—15 participants & - & - & \begin{tabular}{c} Front, side, \\ gown, chest, \\ left lower rib \\ \end{tabular} \\ \hline \begin{tabular}{c} Train \\ augmented \\ model \\ \end{tabular} & WPS—15 participants & 0 to 180 deg. step 20 & Frontal (X-axis), sagittal (Z-axis) & \begin{tabular}{c} Front, side, \\ gown, chest, \\ left lower rib \\ \end{tabular} \\ \hline \begin{tabular}{c} Test \\ holdout \\ \end{tabular} & \begin{tabular}{c} WPS—4 holdout \\ participants \\ \end{tabular} & \begin{tabular}{c} Front, side, \\ gown \\ \end{tabular} \\ \cline{2-5} \cline{3-5}  & \begin{tabular}{c} SHS—20 participants \\ \end{tabular} & \
\begin{tabular}{c} Original, real-life, fully-rotated test sets \\ \end{tabular} &
\begin{tabular}{c} Front \\ \hline \hline \end{tabular}
\end{table}
Table 3: Train and test settings for the baseline and the augmented model computation. The baseline model training did not undergo data augmentation. The two models were tested in the same way by means of three test sets.  
Figure 3: Augmented test set visualization. (**a**) The original sensor orientation compared to the standing human body. (**b**) The applied rotations of the real-life test set along the frontal and sagittal axis. (**c**) The three non-simultaneous rotations applied along the frontal, longitudinal, and sagittal axis (r1, r2, r3) with the fully-rotated test set.  
Each cross-validation fold used the WPS data of ten participants for training the model, the data of two participants for early stopping, and the data of three participants to assess the model performance.  
The activity labels of the two holdout sets were estimated by a majority-voting ensemble of the results of the five models obtained during cross-validation for baseline and augmented models. The results of the original test sets were averaged over the considered participants. The real-life and fully-rotated test sets' results were averaged on the applied external rotations and the considered f1-score was computed by micro-averaging obtained predictions. The performance metrics that evaluated each class were the f1-score, precision, recall, and specificity. Additionally, the Cohen's Kappa (Kappa-score) was considered: it represents an inter-rater agreement coefficient between two raters, as a function of the probability that the two raters are in perfect agreement [31]. For statistical analysis, first the Shapiro-Wilk [32] test was used to verify the normality of f1-score values. Then, the Wilcoxon signed-rank and the t-test were applied to establish differences within performance distributions. The Wilcoxon test is non-parametric, and therefore, does not require normality of the observed data [33].