## 3 Results  
Table 2 summarizes the performance metrics of the various models evaluated in this study, including the threshold-based model, supervised model, and unsupervised models (teacher model, student model, and knowledge distillation (KD) model). The threshold-based model demonstrated an accuracy of 98.86%, indicating its robustness in fall detection. The supervised model achieved an accuracy of 86.29%, showing reasonable performance but less effectiveness compared to the other models. The unsupervised teacher model from the unsupervised approach achieved the highest accuracy of 99.43%, reflecting its superiority in detecting falls. The unsupervised student model, though slightly lower, maintained an accuracy of 92.22%, while the KD model closely followed with an accuracy of 98.86%, demonstrating its ability to maintain strong performance through knowledge transfer. In terms of the sensitivity, which reflects the model's ability to detect falls correctly, the unsupervised teacher model performed well with a sensitivity of 100%, highlighting its effectiveness in identifying all fall events. The threshold-based model performed well with a sensitivity of 97.89%, while the KD model also performed strongly, matching the unsupervised teacher model at 100%. The supervised and unsupervised student models showed relatively lower sensitivities of 88.89% and 88.10%, respectively. Precision, reflecting the accuracy of positive fall predictions, was highest for the threshold-based model at 100%, followed closely by the KD model at 97.93%. The unsupervised teacher model demonstrated a reliable performance with a precision of 98.91%, while the unsupervised student model achieved a precision of 96.10%. The supervised model had a precision of 82.76%. The F1 score, balancing sensitivity and precision, was also high for all models, with the unsupervised teacher model and KD model both achieving scores of 0.99, followed by the threshold-based model at 0.99. The supervised model had an F1 score of 0.86, while the unsupervised student model had a slightly higher F1 score of 0.92. The threshold-based model had the longest lead time at 665.05 \(\pm\) 212.5 ms. The supervised model demonstrated the shortest lead time of 275.46 \(\pm\) 172.87 ms. Among the unsupervised models, the unsupervised teacher model had a lead time of 495.42 \(\pm\) 208.50 ms, followed by the KD model's of 457.1 \(\pm\) 251.76 ms and the unsupervised student model's of 402.2 \(\pm\) 297.91 ms. The KD model demonstrated the smallest memory size of 46 kB. Figure 5 illustrates the confusion matrices for different models. A total of 175 samples were used in the test dataset, consisting of 80 non-fall data points and 95 fall data points. The threshold-based model showed false detections in two fall movements. For the supervised model, a total of 15 movements were classified as false positives, which included 1 normal driving instance being incorrectly detected as a fall and 14 fall movements being detected too late. Due to the early detection of a fall, 1 and 2 false positives were observed in the unsupervised teacher and the KD models, respectively. Early detection refers to instances where a fall is not detected in a timely manner, resulting in a lead time of more than 3 s. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Threshold-Based**} & \multirow{2}{*}{**Supervised**} & \multicolumn{4}{c}{**Unsupervised**} \\ \cline{3-6}  & & & **Teacher** & **Student** & **KD** \\ \hline Accuracy (\%) & 98.86 & 86.29 & 99.43 & 92.22 & 98.86 \\ Sensitivity (\%) & 97.89 & 88.89 & 100 & 88.10 & 100 \\ Precision (\%) & 100 & 82.76 & 98.95 & 96.10 & 97.93 \\ F1 score & 0.99 & 0.56 & 0.99 & 0.92 & 0.99 \\ Lead time (ms) & 665.05 \(\pm\) 212.5 & 275.46 \(\pm\) 172.87 & 495.42 \(\pm\) 208.50 & 402.2 \(\pm\) 297.91 & 457.1 \(\pm\) 251.76 \\ Memory size (kB) & - & 3785 & 1437 & 85 & 46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification performances of models. Table 3 presents the results from the real-road trials.