## 5 Results
### Consideration  
Regarding cases where estimation did not go well, Figure 21 shows that the knee valgus angle estimation results for participant D had significant deviations from the actual values. Figure 22 shows the between-participant cross-validation results for participant D's knee valgus angle during cutting, both before and after calibration. The estimation results before calibration were not as significantly large as those after calibration. Therefore, it is possible that the low estimation accuracy for participant D's knee valgus angle was due to issues with the calibration performed during between-participant cross-validation. Figure 23 shows the scatter plots of the stretch sensor values on the medial side of the knee during flexion and the actual knee valgus angles for each participant. As the knee valgus angle increases, the knee is in a valgus position, and as the knee valgus angle decreases, the knee is in a varus position. From Figure 23, it can be seen that for participants A and B, the knee valgus angle decreases as the stretch sensor value increases, while for participants C, D, and E, the knee valgus angle increases as the stretch sensor value increases. Notably, participant D, who had a low estimation accuracy, showed a significant change in the knee valgus angle. These individual characteristics, such as whether the knee is in a varus or valgus position during flexion, may have caused the calibration to be ineffective.  
Figure 21: Between-participant cross-validation results for cutting by participant D.  
Next, Table 5 shows the mean MAE and RMSE of knee flexion angles for all movements for participants C, D, and E. From Table 5, it can be seen that both the MAE and RMSE are smaller with Feature B, suggesting that when estimating knee flexion angles using data from other users, it is better to use instantaneous values and the past 20 samples of instantaneous values as features. To confirm the estimation accuracy of each machine learning algorithm, we compared the knee flexion angle estimation results using instantaneous values and the past 20 samples of instantaneous values. Figure 24 shows the between-participant cross-validation results for cutting using the RFR and LSVR. In the analysis of the minimum values of the estimation results for participant E in Figure 24, the RFR had a higher accuracy for smaller knee flexion angles.  
Table 6 shows the mean MAE and RMSE of knee valgus angles for all movements for participants C, D, and E. From Table 6, it can be seen that both the MAE and RMSE are smaller with Feature B, suggesting that when estimating knee valgus angles using data from other users, it is better to use instantaneous values and the past 20 samples of instantaneous values as features. To confirm the estimation accuracy of each machine learning algorithm, we compared the knee valgus angle estimation results using instantaneous values and the past 20 samples of instantaneous values. Figure 25 shows the between-participant cross-validation results for cutting using the RFR and LSVR. In the analysis of the maximum values of the estimation results for participant D in Figure 25, the RFR had a higher accuracy for larger knee valgus angles. Additionally, in the analysis of the estimation results for participant D in Figures 21a and 25a, the accuracy of the maximum values in Figure 25a had improved.  
Based on these results, it is suggested that by dividing participants into two groups based on their knee movement characteristics during flexion and creating separate machine learning models, it might be possible to estimate knee flexion and knee valgus angles using models trained with data from other users, using the RFR.  
Figure 24: Between-participant cross-validation results for knee flexion angles during cutting for participants C, D, and E.