## 3 Results
### Session 1: User Group A in Residential Care
#### 3.1.2 Optimizations  
Following an iterative development model in the UX design process, we implemented the optimizations before expert users were confronted with the system in session 2. The main factor influencing the completion rate of the health assessment was the number of overall off-script-events at 34, which led to wrong turns taken in the conversation with the robot, and then subsequent abortions to the interaction. To improve this, we implemented the health assessment questionnaire using the Rasa feature forms, as well as custom actions, for test sessions 2 and 3. If during an interaction with the robot Pepper, the user's verbal input can not be recognized or matched with an intent, a question mark is displayed on the robot's tablet, to prompt the user to repeat and/or rephrase their input. As part of the optimization, we added this information to the introduction of the health assessment. Furthermore, we observed that participants frequently gave premature voice commands, while the robot was transitioning from the introduction to the assessment. As a result, only a part of the input was captured by the robot, leading to false routing between items. To prevent this and improve the success rate of the interactions, we implemented a robot prompt that asked the user to wait two seconds while the robot switches to the health assessment. In session 1 we included only 14 questions plus one conditional question of the health assessment questions in order to first test a simplified version of the assessment. After session 1, we implemented the full 31 questions for the subsequent tests, which are listed in Appendix A.  
We also made several design adjustments to the mobile app. These include an introduction with explanations of how to use the app and visual support to use the app  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Session 1 Participants** & **Session 1 Participants** \\  & **with Experience (N = 5)** & **without Experience (N = 3)** \\ \hline Total turns (w/aborted attempts) & 95 & 53 \\ Successful turns & 79 & 35 \\ Success rate & 83.16\% & 66.04\% \\ Completion rate & 60.00\% & 33.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison of the success and completion rates of the session 1 robot interactions of the participants with and without prior experience with Pepper are shown.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Session 1 w/ Aborted** & **Session 1 w/out Aborted** & **Session 2** & **Session 3** \\  & **Attempts** & **Attempts** & **conditions** & **conditions** \\ \hline Total turns taken (incl. aborted attempts) & 148 & 136 & 167 & 103 \\ Successful turns (incl. aborted attempts) & 114 & 109 & 144 & 84 \\
**Success rate** & **77.03\%** & **80.01\%** & **86.23\%** & **81.55\%** \\ \hline No. of aborted queries & 3 & - & 0 & 0 \\ Total no. of queries (w/out aborted attempts) & & 8 & 5 & 3 \\ Total no. of completed queries & & 4 & 5 & 3 \\
**Completion rate** & & **50\%** & **100\%** & **100\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Success and completion rates of all sessions are shown.  
in both portrait and landscape mode. Finally, we implemented stronger haptic feedback when tapping buttons to increase the awareness of users if they had actually tapped the button correctly.