# Measuring Quality of Service in a Robotized Comprehensive Geriatric Assessment Scenario
## 5 Experimental Evaluation
### Preliminary Experiments
For instance, ten seconds after starting the test the PersonDetected (PD) context is annotated by the Person agent in the DSR, firing the observations two and eight (O2:O8). Both observations reinforce Performance and Interaction (as defined in Listing 3). Then, about two minutes later (t = 135 s), the user answered the first question of the Barthel test in the first attempt using the voice recognition system (fifth column, Table 2). These contexts fire the O13, O2, and O7 observations, increasing the three QoS properties. Observations that decrease these properties are, for instance, O6, O11 that are fired when the patient \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Barthel \#1** & **Barthel \#2** & **Barthel \#3** & **Barthel \#4** & **Barthel \#5** & **[PERSON] \#6** \\ \hline \multirow{4}{*}{**Parameter**} & min & 0.5000 & 0.5000 & 0.1147 & 0.0787 & 0.0017 & 0.0031 \\ \cline{2-8}  & max & 0.9822 & 0.9823 & 0.5882 & 0.6692 & 0.5882 & 0.5882 \\ \cline{2-8}  & avg & 0.8188 & 0.8187 & 0.3351 & 0.3248 & 0.1671 & 0.1357 \\ \cline{2-8}  & sd & 0.1363 & 0.1367 & 0.1474 & 0.2108 & 0.2351 & 0.1944 \\ \cline{2-8}  & last & 0.9822 & 0.9823 & 0.1727 & 0.1686 & 0.0400 & 0.0082 \\ \hline \multirow{4}{*}{**Parameter**} & min & 0.5000 & 0.5000 & 0.1147 & 0.2653 & 0.0303 & 0.0342 \\ \cline{2-8}  & max & 1.0000 & 1.0000 & 0.5882 & 0.8252 & 0.9474 & 0.5882 \\ \cline{2-8}  & avg & 0.9084 & 0.9076 & 0.3349 & 0.6107 & 0.4026 & 0.1893 \\ \cline{2-8}  & sd & 0.1545 & 0.1553 & 0.1477 & 0.1433 & 0.2175 & 0.1776 \\ \cline{2-8}  & last & 1.0000 & 1.0000 & 0.1275 & 0.6938 & 0.9470 & 0.2259 \\ \hline \multirow{4}{*}{**Parameter**} & min & 0.5000 & 0.0000 & 0.000 & 0.4155 & 0.0001 & 0.0006 \\ \cline{2-8}  & max & 1.0000 & 0.5000 & 0.5000 & 0.9141 & 0.5000 & 0.5000 \\ \cline{2-8}  & avg & 0.8870 & 0.1139 & 0.1055 & 0.6870 & 0.1454 & 0.2098 \\ \cline{2-8}  & sd & 0.1859 & 0.1869 & 0.1769 & 0.1711 & 0.1826 & 0.2200 \\ \cline{2-8}  & last & 1.0000 & 0.0000 & 0.0000 & 0.4159 & 0.0001 & 0.0006 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Barthel test results associated to the tests in Table 2 (see text for details). does not answer the question in the first attempt (t = 205 s), or O5 and O10 in the case the patient does not answer the question after the two attempts (t = 340 s). Regarding the Get Up & Go test, we have added to the test-bench nine simulated tests. Table 5 provides a brief description of each test. It can be seen that they consider different execution alternatives, such as the Person agent losing the patient for a few seconds, or the patient calling the doctor during the visual presentation of how she must perform the test. For the Get Up & Go test, the framework only computes two metrics: Performance and Interaction. It is worth noting that the VoiceInteractionQuality is not used in this test because the user does not have to answer any question. Statistical features of the obtained metrics when we execute the nine tests are illustrated in Table 6. Specifically, the table shows the minimum (min), maximum (max), and the final value (last) of the two properties. In contrast to Barthel tests, we have not considered the average and deviation values. The reason for discarding them is that, contrary to the Barthel test where the robot maintains the control over the time slots for answering a question, in the Get Up & Go test, the time is controlled by the patient. That is, a patient has a maximum time of 300 s to perform all the movements on the Get Up & Go test, i.e., sitting on the chair, getting up, walking and sitting on the chair again. But, within this interval of time, she will decide for instance when she sits on or gets up off the chair. As the observations are typically generated in the transition between two actions, the metrics can maintain their values during a few seconds. The result is that the metrics associated with two correctly executed tests can present very different average and deviation features. As the minimum, maximum and last values are not affected by the test duration or the