## 6 Conclusions  
Five basic classification methods in the problem of turn identification and parametrization based on IMU readings have been compared. Their performance has been assessed on the industrial data from the KGHMs underground copper mine, registered on a haul truck operating in an underground mine, during normal operating conditions. Due to an unbalanced sample issue (fragments of the signal from the turns of the vehicle are few in training and testing sample), a scaling mechanism with a parameter equal to 0.15 was used, which vastly improved the results of considered models. Out of the tested logistic regression, linear and quadratic discriminant analysis, SVM, and random forest model, a tree-based model turned out to obtain the best results, with an accuracy score on the scaled test samples reaching over 95%. The obtained sensitivity of the model on the three test samples was 75%, 90%, and over 99%, while specificity has reached at least 97% in each case. Finally, a 5-fold and 10-fold cross-validation was performed, which also confirmed the good results of the random forest model, enabling its implementation into industrial data.  
Detection of a vehicle's turning moments requires a precise examination of those parts of the signal during which very large dynamic overloads take place, often causing damage to the structural nodes of vehicles. The presented methods can be easily extended to the  
Figure 7: Result of the final model: (**A**) Visual representation of results; (**B**) 5-fold cross-validation; (**C**) 10-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Sample No. 2**} & **Sample No. 3** \\ \hline Accuracy score & 0.9846 & 0.9734 \\ \hline Confusion matrix & \(\left[\begin{array}{cc}41,631&364\\ 334&3082\end{array}\right]\) & \(\left[\begin{array}{cc}42,736&1219\\ 3&2003\end{array}\right]\) \\ \hline Sensitivity & 0.90 & 1.00 \\ \hline Specificity & 0.99 & 0.97 \\ \hline Negative predictive value (NPV) & 0.89 & 0.62 \\ \hline Precision (PPV) & 0.99 & 1.00 \\ \hline F1 score & 0.90 & 0.77 \\ \hline Macro average (precision) & 0.94 & 0.81 \\ \hline Weighted average (precision) & 0.98 & 0.98 \\ \hline Macro average (recall) & 0.95 & 0.99 \\ \hline Weighted average (recall) & 0.98 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the selected random forest model on two additional testing samples.  
similar problem of the detection of route ascents/descents, but the feature vector would have to be based on readings from a different axis of the gyroscope.  
Conceptualization, P.S. and B.J. methodology, J.W., B.J., and P.S.; software, J.W. and A.S.; validation, J.W., B.J., A.S., P.S. and M.D.; formal analysis, J.W., B.J. and P.S.; investigation, J.W.; resources, P.S. and P.S.; data curation, J.W.; writing--original draft preparation, J.W., B.J., A.S. and P.S.; writing--review and editing, J.W., B.J., A.S. and P.S.; visualization, J.W. and A.S.; supervision, P.S.; project administration, P.S. and P.S.; funding acquisition, P.S., P.S. and M.D. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the European Union's Horizon 2020 research and innovation program, grant number 869379.  
Not applicable.  
The authors declare no conflict of interest.