## 4 Discussion  
In this study, falls during e-scooter riding were detected using an IMU sensor before the impact occurred at the ground. Threshold-based, supervised, and unsupervised models were developed. In addition, the lightweighting of the model was achieved using knowledge distillation in the unsupervised learning approach. The threshold-based model achieved an accuracy of 98.86%, effectively distinguishing between falls and normal driving, with an F1 score of 0.99. Two false negatives in the threshold-based model occurred likely due to the foot touching the ground before the fall, which delayed the change in angular velocity; however, these two FNs were successfully detected by unsupervised learning-based teacher and KD models. The threshold-based model also exhibited the longest average lead time of 665.05 ms. It is expected to use less computational memory compared to AI models. In the case of the supervised model, the performance was somewhat lower, with an accuracy of 86.29%, mainly due to the imbalance between the normal and the fall data. Although undersampling techniques were applied to minimize this imbalance, the number of fall data samples was still limited. Therefore, it will be essential to collect more fall data in future work to improve training and model performance. The unsupervised learning-based teacher and KD models both recorded an accuracy of over 98%, successfully detecting falls. The false-positive errors in the unsupervised learning teacher and KD models occurred because of the early detections. Although their lead times were slightly shorter than that of the threshold-based model, they still provided sufficient time for deploying protective devices such as airbags [24]. Figure 5: Confusion matrices in different models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Threshold-Based**} & \multirow{2}{*}{**Supervised**} & \multicolumn{3}{c}{**Unsupervised**} \\ \cline{3-5}  & & **Teacher** & **Student** & **KD** \\ \hline TN & 50 & 50 & 50 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance on real-road driving data. In the case of normal driving in the real world, all models demonstrated 100% specificity, showing good performance despite the potential sensor noise that may occur on the road surface. The algorithms developed in this study are expected to be applied to protective devices, such as wearable airbags, to enhance the safety of riders. [PERSON] et al. [19] reported an accuracy of 97.1% and an F1 score of 0.970 in their study on fall detection using inertial sensors attached to an e-scooter. They employed TinyML to integrate machine learning models into edge devices, focusing on aspects like computation time. However, their study did not address lead time, which is crucial for detecting falls before impact. [PERSON] et al. [25] developed a fall detection algorithm based on IMU embedded in motorcycles and validated it through 29 scenario movements. They reported a lead time of over 260 ms, which is sufficiently longer than the 100 ms required for rider jacket deployment. In this study, the lead time of all models was sufficiently long. The KD model successfully reduced the memory size by approximately 96%, compared to the unsupervised teacher model, down to 46 kB. The memory sizes in Table 2 were expressed in the h5 Keras format, but can be further reduced when converted to TensorFlow Lite (.tflite). Thus, owing to the knowledge distillation technique, a lightweight model was created while maintaining a certain level of performance of the teacher model, possibly making it suitable for application on edge devices such as smartphones. This study has several limitations. It is extremely dangerous to measure actual falls or to induce them; thus, fall data are generally collected artificially. Another limitation is that most participants were in their early twenties, which makes it difficult to generalize the findings to other age groups.