## 4 Discussion
One reason may be that the random errors in the OWT were generally small and less than 1 s. Interestingly, some participants stated that completing the task with the robot using the Kalman filter prediction method was more \"like interacting with another human operator\". Interacting with a robot that applies the trigger sensor approach results in the smallest and most 'predictable' random error in OWT and RWT, \"like pushing a button and getting a response from the robot\". Participants usually related this experience to'reactive' and 'not autonomous'. On the other hand, when interacting with the robot using the Kalman filter prediction method, the variations in OWT and RWT were generally less predictable and this small variation in the Kalman filter predictions, when compared with the almost fixed random error in the trigger sensor context, may have enhanced the participants' acceptance of the Kalman filter prediction model. The subjective questionnaire assessed the participants' acceptance in terms of \"fluency\" by using the items \"The robot and I work well together\", \"Deliveries made by the robot are smooth\", and \"I work fluently with the robot\". The two experiments conducted in the present study verified that when the OWT was lower, the participants were more likely to regard the robot as a team member and subjectively felt greater fluency in the HRI. Therefore, in a collaborative environment, reducing the OWT through effective prediction can help operators accept robots as normal working partners in the work environment. This study assumed that HRI was limited to a simple handover and investigated the cycle time, waiting time, and subjective preference of operators. This simple handover context revealed that the RWT did not affect performance and subjective preference. However, in actual manufacturing, HRI is not limited to simple handovers, because robots usually have to perform other tasks in addition to handover tasks. Therefore, this limitation was ignored in this study. The effects of the OWT and RWT on performance and whether the Kalman filter prediction model is superior are research topics worthy of further investigation. In this study, the Kalman filter prediction model was implemented at the task level; that is, the time measurement value \(z_{k}\) of each completed assembly was the input of the current state. The real-time assembly movement speed could feasibly be used as an input with the PhaseSpace measurement method, which should result in improved stability of predictions. Although measurements are not easy to perform using conventional motion capture equipment due to the high cost of their implementation on production lines, if the assembly motion speed can be confirmed as an input that improves prediction stability, image and speed information could be captured using a relatively low-cost RGB 3D camera and Leap motion gesture sensing device. In addition, if the model were integrated with an artificial intelligence and deep learning algorithm (e.g., a convolutional neural network or long short-termmemory), the assembly movement speed could be predicted, and the Kalman filter could be used to predict the cycle time of human-robot collaboration in an assembly task. It may be questioned whether the choice of a fixed standard time for all those experiments that employ the MTM approach--here set to 8.7 \(\mathrm{s}\)--is distorting the results of the user experience assessment. For example, one may assume that participants with different skill levels should be assigned different cycle times that reflect their level, or that in general a shorter cycle time would be appropriate for this simple handover task. A shorter cycle time and, subsequently, a shorter OWT would have resulted in a better evaluation of working with the MTM approach. Two defenses of the strategy chosen in this study shall be given: First, it would be inappropriate to assume an exclusive and direct positive correlation between the cycle time being shorter and the user being more satisfied.