## 6 Discussion  
In this paper, we proposed a system that uses stretch sensors attached to a knee supporter to measure the knee angles during practice in order to notify them of ACL injury risk. Based on the evaluation results of the knee flexion angle estimation accuracy experiment, the MAE was 0.81\({}^{\circ}\) when using a model trained with the RFR on the user's own data. Experts indicated that in rehabilitation settings, estimating knee valgus angles of 10\({}^{\circ}\) or more would be practical for actual use. In this experiment, there were few movements where the knee valgus angle changed by 10\({}^{\circ}\) or more. However, during the cutting motion for participant D, as shown in Figure 25, the knee valgus angle was approximately 10\({}^{\circ}\), suggesting that it might be possible to estimate knee valgus angles of 10\({}^{\circ}\) or more even in \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RFR** & \multicolumn{2}{c}{**LSVR**} \\ \hline  & **Feature A** & **Feature B** & **Feature A** & **Feature B** \\ \hline MAE [\({}^{\circ}\)] & 2.14 & 2.01 & 1.87 & 1.83 \\ RMSE [\({}^{\circ}\)] & 2.57 & 2.38 & 2.11 & 2.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Estimation errors of knee valgus angle in between-participant cross-validation for participants C, D, and E.  
Figure 25: Between-participant cross-validation results for knee valgus angles during cutting for participants C, D, and E.  
between-participant cross-validation. Although it cannot be concluded that the current experiment can estimate conditions that would likely result in ACL injury, the results suggest that by creating training data with groups exhibiting similar flexion tendencies, it might be possible to estimate knee valgus angles of 10\({}^{\circ}\) or more using only other users' data. Since movements that result in knee valgus angles changing by 10\({}^{\circ}\) or more could potentially cause ACL injuries, it is challenging to collect such data. Therefore, in future work, we will create a knee model and collect data on knee joint angles in conditions where the knee bends enough to cause injury to verify if the system can estimate knee angles that would result in actual ACL injuries. In this study, the MAE for the knee flexion angle was approximately 3.3 degrees, comparable to the range of 1.6 to 4.2 degrees reported in [PERSON] et al. 's study [26]. Furthermore, this paper uniquely contributes by also estimating the knee valgus angle. Additionally, as an injury prediction system, the ability to obtain moment-to-moment knee angles is an advantage, allowing for the specific analysis of movements that may lead to injuries. In this experiment, all participants used the same M-sized supporter. If the proposed system's device comprised supporters of different sizes, the stretch sensor values might differ from the current training data even if the sensors were placed in the same positions. However, the results indicate that knee flexion and valgus angles can still be estimated using other users' training data after calibration, even if the stretch sensor values vary among participants. Therefore, it is considered that knee flexion and valgus angles can be estimated with the same training data even for different sizes of supporter-type devices. In this experiment, the ground truth knee joint angles were obtained using VICON [17], necessitating laboratory conditions. Consequently, participants might not have been able to run at full speed, and the current training data may not directly apply to actual running motions at higher speeds. If this is the case, using stretch sensors with a higher sampling frequency than the current 100 Hz for the supporter-type device might address the issue. The supporter-type device used three stretch sensors. Although increasing the number of stretch sensors might improve the estimation accuracy, it would require additional wireless modules to transmit the sensor data, potentially increasing the device's weight and hindering user movement.