## 3 Results  
### Experiment 1: Repetitive Assembly  
Experiment 1 investigated the effects of the handover prediction model and assembly speed on cycle time and waiting time in mass production mode. Table 2 shows the average cycle time for the three skill level groups under the three handover prediction models. According to the analysis of variance (ANOVA) of cycle time, the two main effects of handover prediction model and skill level were significant (F(2,66) = 17.51, \(p<0.001\) and F(2,33) = 233.09, \(p<0.001\), respectively). The post hoc test was used to verify the levels of the two main effects, and the three skill levels were discovered to be highly significant (\(p<0.001\)). Among the three handover prediction models, the cycle time was significantly higher when the MTM was used rather than the trigger sensor method and Kalman filter (\(p<0.001\)); the difference between the cycle times for the trigger sensor method and Kalman filter was nonsignificant (\(p>0.5\)). According to Figure 5, the participants with higher skill level had a shorter average cycle time. For the trigger sensor method and Kalman filter, the participants of all skill levels had almost identical performance. However, the cycle time when the MTM was used was not positively affected by the higher efficiency of the more skilled participants because the robot's handover cycle time was predetermined at 8.7 s, causing the cycle time to remain close to that of the intermediate group. According to ANOVA, the interaction between prediction model and skill level was highly significant (F(4,66) = 14.66, \(p<0.001\)). Furthermore, the post hoc test verification demonstrated that the cycle time did not differ between the three handover prediction models for the beginner and intermediate groups (\(p>0.5\)). Nonetheless, for the advanced group, the cycle time was significantly higher for the MTM than for the trigger sensor method and Kalman filter (\(p<0.001\)), whereas the difference in cycle time between the trigger sensor method and Kalman filter was nonsignificant (\(p>0.5\)). The second part of the experiment concerned the waiting time, which was divided into the OWT and RWT. According to Figure 6, the three skill level groups had similar OWTs, approximately 0.1 s, for the trigger sensor method and Kalman filter. However, when the MTM was employed, the OWT of the beginner group was 0 s, indicating that the cycle time for all tasks was within 8.7 s, resulting in no OWT. By contrast, for the advanced group, almost every task resulted in the operator having to wait for the robot; thus, the average OWT was 1.2 s. The results of ANOVA indicated that the effect of handover prediction model on OWT was highly significant (F(2,66) = 69.11, \(p<0.001\)), as was that of skill level on OWT (F(2,33) = 77.23, \(p<0.001\)). The interaction between model and skill level was also highly significant (F(4,66) = 69.84, \(p<0.001\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Method**} \\ \hline
**Skill Level** & **MTM** & **Kalman Filter** & **Trigger Sensor** & **Average** \\ \hline Novel & 9.86 & 9.63 & 9.80 & 9.76 \\ Intermediate & 8.71 & 8.78 & 8.76 & 8.75 \\ Advanced & 8.71 & 7.80 & 7.73 & 8.08 \\ Average & 9.09 & 8.74 & 8.77 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average cycle time for three skill level groups under three handover prediction models. Figure 5: Average cycle time for three skill levels and handover prediction models. Figure 7 plots the RWT and shows that when the trigger sensor method was employed, the three groups had fairly similar RWTs of approximately 0.1 s. When the Kalman filter was used, the RWT decreased slightly from 0.65 to 0.51 s as the skill level increased, but the RWT difference between the three skill level groups was not large. When the MTM was employed, the RWT dropped significantly from an average of 1.17 to 0.01 s as the skill level increased. The RWT was high for the beginner group but RWT almost 0 for the advanced group.