## 3 Results
### Experiment 2: Assembly Learning Effect  
In Experiment 2, the cycle time was discovered to decrease due to the learning effect in a longer period of assembly. In addition, the effects of the three handover prediction models on the operator's cycle time and waiting time were compared.  
According to Figure 10, the overall average cycle time for 20 assembly sessions when the MTM, trigger sensor method, and Kalman filter were used was 9.11, 8.60, and 8.58 s, respectively. The cycle time for the MTM was significantly larger than that for the other two models. Between-participants ANOVA revealed that the main effect of the handover prediction model was highly significant (F(2,33) = 5.58, \(p<0.01\)). In addition, the Tukey post hoc test showed that the average cycle time was significantly larger when the MTM was used compared with the trigger sensor method and Kalman filter (\(p<0.05\)), but no difference was discovered between the trigger sensor method and Kalman filter (\(p>0.1\)).  
Figure 11 illustrates the change in the average assembly time over time (20 sessions). The assembly time clearly decreased over time when the trigger sensor method and Kalman filter were employed, which is in agreement with the learning effect, according to which the cycle time should show a downward trend. When the MTM was employed, the cycle time again exhibited a downward trend when the assembly cycle time of the participants in the previous phase was greater than the predetermined cycle time (8.7 s). However, even when the assembly speed of the participants was higher in the later phase, the cycle time could not drop below the fixed handover cycle time (8.7 s; as shown by the dotted line in Figure 11). ANOVA revealed a highly significant effect of the prediction model (F(19,627) = 79.75, \(p<0.001\)), and the interaction term between the session and prediction model was also highly significant (F(38,627) = 6.09, \(p<0.001\)].  
Figure 10: Cycle time for three handover prediction models.  
From the perspective of waiting time, neither the RWT (Figure 12) nor OWT (Figure 13) significantly changed over time when the trigger sensor method and Kalman filter were employed; instead, the waiting times were randomly varying in each session. However, for the MTM, the RWT clearly decreased, whereas the OWT gradually increased in the later stage, which corresponds to the trend change displayed in Figure 11, thereby affecting the average cycle time.  
The participants' subjective preferences were compared for the learning effect experiment (Experiment 2). The median score and 95% confidence interval for the three handover models  
Figure 11: Cycle time over 20 sessions for different handover prediction models under the assembly learning effect.  
Figure 12: Time variation of the RWT for different handover prediction models under the assembly learning effect.  
Figure 13: Time variation of the OWT for different handover prediction models under the assembly learning effect.  
are displayed in Figure 14. The participants' preference score for the MTM was significantly lower than that for the other two models. The Kruskal-Wallis rank-sum test was used to analyze the ordinal data of subjective preference, and the results revealed that handover prediction model (x\({}^{2}\) (2) = 25.93, \(p<0.001\)) had a highly significant effect. Furthermore, post hoc verification was performed through the Wilcoxon rank-sum test, and the findings showed no significant difference between the trigger sensor method and Kalman filter (\(p>0.1\)), but both were preferred by the participants over the MTM (\(p<0.001\)).