## 3 Results
### Final System Evaluation  
For the dataset of the evaluation experiment with 1951 punch samples of eight athletes, the results were as follows: accuracy of 89.55% for the striking technique and 75.97% for the binary target object classification. Besides the overall accuracy, it was also evaluated which striking techniques were recognized most precisely. For this, the confusion matrix (see Figure 4) of the striking techniques was computed together with the classification report per striking technique (Table 9). Therefore, the confusion matrix can be viewed in addition to the accuracy results to assess the distribution of the true positive and false positive instances indicating which techniques were most often confused by the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Baseline Accuracy Result** & **Optimized Accuracy Result** \\ \hline RF & 94.36\% & 94.50\% \\ kNN & 95.37\% & 96.96\% \\ MLP & 97.97\% & 97.97\% \\ SVC & 95.80\% & 98.26\% \\ \hline \hline \end{tabular} (RF = Random forest; SVC = Support vector classifier; kNN = k-nearest neighbor; MLP = Multi-layer perceptron).  
\end{table}
Table 6: Predictive performance for the target object classification computed using 10-fold cross-validation on the test dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier 1** & **Classifier 2** & \(p\)**-Value** & \(t\)**-Statistics** \\ \hline RF & SVC & 0.028 & \(-\)3.061 \\ RF & kNN & 0.156 & \(-\)1.668 \\ RF & MLP & 0.111 & \(-\)1.932 \\ kNN & SVC & 0.922 & 0.103 \\ kNN & MLP & 0.209 & 1.442 \\ SVC & MLP & 0.031 & 2.961 \\ \hline \hline \end{tabular} (RF = Random forest; SVC = Support vector classifier; kNN = k-nearest neighbor; MLP = Multi-layer perceptron).  
\end{table}
Table 7: Significance test results of the model comparison for the striking technique classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier 1** & **Classifier 2** & \(p\)**-Value** & \(t\)**-Statistics** \\ \hline RF & SVC & 0.002 & \(-\)5.700 \\ RF & kNN & 0.008 & \(-\)4.260 \\ RF & MLP & 0.001 & \(-\)6.462 \\ kNN & SVC & 0.105 & \(-\)1.977 \\ KNN & MLP & 0.486 & \(-\)0.751 \\ SVC & MLP & 0.143 & 1.735 \\ \hline \hline \end{tabular} (RF = Random forest; SVC = Support vector classifier; kNN = k-nearest neighbor; MLP = Multi-layer perceptron).  
\end{table}
Table 8: Significance test results of the model comparison for the target object classification.  
In addition, it was assessed if there was a difference between the individual participant groups by computing the accuracy, averaged over the participants for each group (Figure 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Straight & 0.98 & 0.92 & 0.95 & 776 \\ \hline Hook & 0.81 & 0.83 & 0.82 & 434 \\ \hline Uppercut & 0.85 & 0.95 & 0.90 & 475 \\ \hline Backfist & 0.85 & 0.95 & 0.89 & 139 \\ \hline Ridge Hand & 0.96 & 0.74 & 0.84 & 127 \\ \hline \multicolumn{4}{c}{Overall} \\ \hline macro avg & 0.89 & 0.88 & 0.88 & 1951 \\ \hline weighted avg & 0.90 & 0.90 & 0.90 & 1951 \\ \hline \end{tabular}
\end{table}
Table 9: Classification report including precision, recall, \(F1\)-score and support (number of samples) per class for the striking technique classification results.  
Figure 4: Confusion matrix for the striking technique classification evaluation showing the number of true positive and false positive instances.