## 4 Discussion  
The results of Experiment 1 revealed that the subjective preference and objective average cycle time of participants of all skill levels for the three handover prediction models were highly consistent. No significant differences were discovered in either objective measurement or subjective preference between the beginner and intermediate groups for the three prediction models. However, for the advanced participants, the average cycle time was significantly lower and subjective preference were significantly superior when the Kalman filter was used compared to the MTM. In addition, no statistical differences were discovered between the performance of the Kalman filter and the trigger sensor method. This nondifference in performance is explained by the waiting time analysis. Since an OWT constitutes a negative interference with the fluency of an operation, a greater OWT not only results in a longer average cycle time but also lower preference. According to a comparison between Figures 6 and 9, a shorter OWT resulted in higher preference. By contrast, a comparison between Figures 7 and 9 reveals that the RWT was uncorrelated with subjective preference. The results suggest that no interference occurred in the HRI because the RWT did not affect the cycle time. Therefore, a longer RWT does not affect the task completion time and subjective preference. This finding is consistent with [19], who also concluded that human-robot team fluency was not correlated to robot idle time. Experiment 2 explored the effects of the three handover prediction models on the learning curve of repetitive assembly. Similar to the results of Experiment 1, the average cycle time and subjective preference revealed that the Kalman filter was superior to the MTM, and no statistical difference was discovered between the Kalman filter and trigger sensor method. According to a comparison between Figures 11 and 13, the OWT was again discovered to be the key factor affecting the cycle time. Moreover, the result illustrated in Figure 14 shows that the subjective satisfaction with the MTM was lower probably because of the higher OWT in the later phase of assembly. Combining the two experiments revealed that in the repetitive assembly task, in which either a stable average assembly speed was accompanied by nonattributable random errors or there was  
Figure 14: Median preference score for the three handover prediction models. a gradual change in the causable assembly speed, the Kalman filter was superior to the MTM and resulted in the same performance as the trigger sensor method. Using the five formulas of the Kalman filter and parameter settings, the system can be employed to recursively predict the next value using the preceding measured value. The R in this study was set to 0.0042 after several pretests. If the set value is too large, the correction of the system is slow, and the system lags behind the gradually changing assembly speed. By contrast, if the set value is too small, the system is overly sensitive. If the previous assembly was too slow due to a nonattributable random error, this information is quickly reflected in the next assembly time prediction, resulting in a longer prediction time for the next cycle, which is obviously not the behavior of an accurate system. The Kalman filter effectively predicted handovers in the repetitive assembly tasks used in this study under appropriate parameter settings. All predictions had errors that led to an RWT or OWT, and those made using the Kalman filter were no exceptions. As aforementioned, the RWT had no effect on the cycle time and subjective preference in the investigated assembly task; rather, the key was the OWT. Figure 6 shows that OWT for the Kalman filter, apparently, was larger than that of the trigger sensor method. However, the participants did not appear to dislike the Kalman filter, as evidenced by the subjective preference ratings reported by the participants.