# A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone
## 2 Related Work
The clinical expert that conducts the assessment assigns scores for the mobility, cognitive status and other health aspects. The assistive technology is then programmed to map these scores to the data collected from the sensors during the respective period of assessment, with the aim to predict future health scores starting from the sensors data. This strategy is used in Ref. [33]. * Classification approaches. These are entirely similar to the discriminating strategy described above. * Outlier detection approaches assume that the training data define the normal behavior and compare subsequent ADL data with the baseline defined in the training phase. Significant deviations from this baseline (outliers) are considered abnormal behavior. This approach is used in Ref. [34]. Though the solution proposed in the present study does not fit exactly into any of the categories described above, there are multiple similarities with previously published works. For example, the concept of activity map was used in Ref. [35] as a visual representation of the intensity of ADLs over time, for the use of a human expert. The solutions based on this type of activity maps use exclusively the temporal features of the sensor data and search for patterns in the frequency and the sequential order of sensor firings, thus missing important behavioral clues that derive from the spatial context of the activities. This limitation is eliminated in Ref. [36], where the activity maps are visual images indicating the time spent in certain locations. Though the resulting maps are richer in information about the users' behavior, this work does not propose a method to process this information for automatic anomaly detection. A different approach is proposed in Refs. [37; 38], where indoor localization systems and digital pheromones are used to create a visual map of the living environment. These maps embed traces that reflect the motion of the monitored persons, and it is assumed that the normal behavior is encoded in a set of preferred motion routes. The work described in Ref. [39] also use virtual pheromones to create activity maps, with the difference that the pheromone sources are attached to the sensors in known locations. Every time a sensor is triggered by the activity of the user, the pheromone source releases a fixed amount of pheromones, which diffuse in space and evaporate with time. Thus, the images reflecting the distribution of the pheromones over the living space contain both information about the intensity of the activity of the inhabitant and about the place where the activity occurred. Behavior patterns are then identified by comparing the pheromone-based activity maps using a similarity measure. The idea of an abstraction layer to overcome the variability of the spatial distribution of the sensors is introduced in Ref. [40] in the form of \"labels\" indicating the location, attached to the sensor events. Finally, the idea of using fuzzy reasoning to estimate the deviations from the ADLs routine appears in Ref. [41]. Here, the monitored person's daily routine is extracted from data about the usage of household appliances and deviations from this routine are detected by means of a set of fuzzy rules. Unlike most of the existing studies on detecting behavior changes, the solution proposed in the present work _does not use machine learning_. We start by creating an abstraction of the living environment in the form of a set of behaviorally meaningful places, located symmetrically in a generic Cartesian space. Then, we use the data provided by a set of binary sensors to create activity maps that embed information about the intensity and the spatial distribution of the activities of the monitored person. Finally, we use the information about the relative weights of the BMPs in the structure of the activities to detect deviations from the activity routine, following a method described in the next section.