**6. Experiments**

conversion of NUIs to GES and GEF that are interpretable to geriatricians. However, monthly statistics in some cases do not capture important within month variations in time series.

This is why, in contrast to NUIs, clusters are not restricted to monthly level. Depending on input data, clusters can be identified on daily or monthly level. For example, if number\_ of\_steps activity measure is clustered over time, model can identify similar groups of daily values: days with high values (i.e. average of 3000 steps with standard deviation of 200 steps) and days with low values (i.e. average of 600 steps with standard deviation of 100 steps). Similarly, cluster models can identify patterns of series of GES or GEF. For example: care recipient have periods of time where motility have average motility value of 4.1 with standard deviation of 0.2. So, behavioral patterns encapsulated in cluster models provide characterization of behavior on finer grade than monthly level. Additionally, the level of granularities

For example, in first 22 days of January, care recipient had high values and high variability of number\_of\_steps, but in next 12 days he or she had low values with low variability. Even though, behavioral patterns described by clusters are not necessarily aligned with monthly representations of NUIs, GES and GEF, they can be exploited for definition of new NUIs that will capture within month behavior variations (e.g., care recipient showed improved behavior in last eight days of a month). NUIs based can be further graded as described in previous sub-section. Based on previous examples it is intuitively clear that clusters (behavior patterns) encapsulate smaller variations of time series and allows data driven discretization and characterization of discrete categories. This discretization allows easier inspection of behavioral changes (than observing unclustered series with many variations) and thus, results of clustering (cluster labels) can be directly represented on City4Age interactive dashboards or used as NUIs for derivations of GES and GEF (**Figure 5**). This way Geriatricians can access visualizations of natural groupings among series data, and label behaviors (e.g., normal, bad or good

It is important to emphasize that grouping of activities and/or citizens will play an important role in extending of City4Age interactive dashboards, with mentioned easier identification

does not have to be defined in advance (e.g., weeks).

124 Recent Applications in Data Clustering

or on the Likert scale) or revise data driven grading of clusters.

**Figure 5.** Cluster model flow.

In order to evaluate proposed framework we conducted experiments collected from City4Age Pilot sites. The main goal of the experimental evaluation was to evaluate usefulness of HMM models for behavior recognition, behavior change and anomaly detection in context of City4Age IoT data. We will describe process of data collection, modeling and evaluation in following text.
