We present a model for the analysis of time series sensor data collected at an eldercare facility. The sensors measure restlessness in bed and bedroom motion of residents during the night. Our model builds sets of linguistic summaries from the sensor data that describe different events that may occur each night. A dissimilarity measure produces a distance matrix D between selected sets of summaries. Visual examination of the image of a reordered version of D provides an estimate for the number of clusters to seek in D. Then, clustering with single linkage or non-Euclidean relational fuzzy c-means produces groups of summaries. Subsequently, each group is represented by a linguistic medoid prototype. The prototypes can be used for resident monitoring, two types of anomaly detection, and interresident comparisons. We illustrate our model with real data for two residents collected at TigerPlace: the “aging in place” facility in Columbia, MO, USA.