## 3 Results
### Session 1: User Group A in Residential Care  
In session 1, we tested our system with user group A, which consisted of eight residents of a nursing home. Additionally, one of their caregivers also participated in the study in this session. The results in Table 1 include only the eight older participants, as they belong to the target group of older adults. Overall, the user experience of the health monitoring system was rated highly in session 1. The KPIs, with a possible value between \(-3\) and \(3\), are all above \(2\), with the interaction with the robot Pepper being rated the highest (2.88) and the connected health devices rated the lowest (2.25), and the app in between at 2.71. This results in an overall system KPI of 2.51. In addition, the caregiver also gave high ratings, with an overall system KPI of 2.50, indicating high UX from the care perspective, also. Regarding the use of health devices for measurements, we observed that some of the participants struggled to put on the wearable and the blood pressure monitor by themselves. All participants were able to correctly carry out the body temperature measurement, while some had problems with sending the measured value to the server. This was performed using a button on the thermometer. After another explanation and/or demonstration, most of the participants managed to both measure and send the measured value to the server correctly. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Session 1—**Group A in residential care, N=8 + 1, 5 + 1 with previous Pepper experience** \\ \hline
**Perspicuity** & **Mean (SD)** & **Median** & **Importance (SD)** & **KPI (SD)** & **Range** \\ Pepper & 2.45 (0.59) & 2.5 & 2 (0.63) & 2.45 (0.48) & \(-1.75\)–\(3\) \\ Sensors & 1.55 (1.28) & 1.5 & 2.67 (0.49) & 1.55 (0.66) & 0.75–\(2.75\) \\ App & 2.35 (1.35) & 3 & 2.2 (0.75) & 2.35 (1.3) & 0–\(2.5\) \\ \hline KPI Overall (SD) & & & & 2.1 (0.64) & 0.83–\(2.54\) \\ SUS Overall (SD) & & & & 85 (4.18) & 80–\(90\) \\ \hline \hline
**Session 3—**Group C in assisted living, N = 3, 3 with previous Pepper experience** \\ \hline
**Perspicuity** & **Mean (SD)** & **Median** & **Importance (SD)** & **KPI (SD)** & **Range** \\ Pepper & 1.33 (1.97) & 2 & 1.33 (1.25) & 1.33 (1.25) & 1.25–\(3\) \\ Sensors & 1.42 (1.61) & 2 & 0.75 (1.41) & 1.42 (1.05) & \(-0.25\)–\(3\) \\ App & 1.92 (0.86) & 2 & 0.33 (2.05) & 1.92 (0.51) & 1.25–\(2.5\) \\ \hline KPI Overall (SD) & & & & 1.45 (0.94) & 0.36–\(2.67\) \\ SUS Overall (SD) & & & & 67.5 (16.20) & 45–\(82.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The overall results of the UEQ+ and SUS questionnaire are displayed. When using the data visualization app, some participants tapped the buttons in such a manner, that the app did not respond, e.g., due to tapping the button for too long. They, therefore, had to repeatedly tap the button for it to lead to its corresponding action or page. Overall, most of the participants were able to find the values we asked them to look for, i.e., their body temperature and blood pressure. One participant stated that they would prefer to use the app on a computer. This is also possible with this application, as it was developed using a multi-platform framework. Additionally, almost all participants also stated that they had a positive impression of both the devices and the app. In the context of a health assessment, which is intended to relieve care professionals of documentation tasks, both data accuracy and data completeness are crucial. We evaluated data accuracy by calculating the success rate, which we define as the number of error-free interaction turns in relation to the total number of turns in a session. In this evaluation, we considered the actual health assessment, only, and excluded the introduction. We defined a turn as the complete sequence of a robot question, the user response, and the subsequent robot feedback. In session 1, over the course of all assessments of the eight older users, we recorded a total of 148 turns, of which 114 were error-free.