## 3 Results
### Experiment 1: Repetitive Assembly
The results of ANOVA revealed that the RWT was significantly affected by the handover prediction model (F(2,66) = 57.20, \(p<0.001\)) and skill level (F(2,33) = 26.27, \(p<0.001\)). In addition, the interaction term was highly significant (F(4,66) = 28.95, \(p<0.001\)). Figure 8 displays the total waiting time (i.e., the sum of the OWT and RWT). The lowest total waiting time occurred when the trigger sensor method was employed, followed by the Kalman filter. The MTM resulted in the highest total waiting time in the beginner and advanced groups. Figure 6: Average operator waiting time (OWT) for the three skill level groups and handover prediction models. Figure 7: Average RWT for the three skill levels and handover prediction models. Although the total waiting time was higher for the Kalman filter than the trigger sensor method, the average cycle time (Figure 5) showed that these two models resulted in almost identical performance. The main factor was that in the investigated collaborative process, the robot only participated in the handover of assembly task; the final assembly task had to be completed by the operator. Hence, the cycle time of the entire operation was limited by operator-induced interference, with the waiting time prolonging the overall cycle time. Conversely, robot-induced interference did not affect the total task completion time. According to Figure 6, the OWT was almost the same, approximately 0.1 s, for all skill groups when the trigger sensor method and Kalman filter were used. This explains why the cycle time was almost identical even though the waiting time was slightly longer for the Kalman filter method than the trigger sensor method. In the subjective preference analysis of Experiment 1, the median scores and 95% confidence intervals of the participants' subjective preference for the three handover prediction models are displayed in Figure 9. The advanced group had significantly lower preference for the MTM than the other two groups. The preference scores of all participants for the trigger sensor method and Kalman filter were relatively close, but the preference score for the Kalman filter increased as the skill level increased. Two-way repeated ordinal regression was used to analyze the ordinal data of subjective preference, in which the prediction model and skill level were the independent variables, and the participants were used as blocking variables and were entered as a random variable. The results demonstrated that both  
Figure 8: Average total waiting time for the three skill levels and handover prediction models. Figure 9: Median preference scores of participants in three skill level groups and for three handover prediction models. prediction model (x\({}^{2}\) (2) = 42.12, \(p<0.001\)) and skill level (x\({}^{2}\) (2) = 8.05, \(p<0.05\)) had significant effects, and the interaction term of these two factors was also highly significant (x\({}^{2}\) (4) = 69.09, \(p<0.001\)). Due to the fact that the interaction term was highly significant, further Tukey post hoc tests were performed on the nine experimental combinations of three skill levels and three handover prediction models. The results revealed that at the 95% confidence level, the preference of the advanced group for the MTM was significantly lower than that for the other eight experimental combinations.