# A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone
## 2 Related Work  
Automatic detection of anomalies in human behavior is a challenging task because the behavior of every individual is influenced by age, gender, education, cultural habits, etc., and characterized by a unique spatio-temporal distribution of activities. Further challenges in detecting activity patterns are posed by contingent factors like:  
* Parallel or interleaved execution of certain activities (e.g., a person may cook dinner, watch TV, and answer the phone at the same time). * Periodic variations. The structure of the ADLs may be subject to weekly, monthly, or seasonal variations (e.g., sleep hours may be different during winter and summer). * False starts. Sometimes people start an activity and suddenly abandon it for unexpected reasons. Moreover, the environment where people live is also extremely diverse, which leads to a great diversity in what concerns the type and number of sensors used for monitoring, and their spatial distribution. Therefore, we believe that the main challenge for the research on this topic is not to find solutions with high accuracy of the detection (such solutions already exist), but to design monitoring systems easily adaptable for any individual and for any living environment. The present work is an attempt to contribute to the progress of the research in this particular direction. As shown in Refs. [27; 28], three main strategies have been described in the literature to solve the problem of detecting anomalies of the human behavior (see Figure 1):  
In the Activity Recognition Strategy, depending on the granularity of the desired output, we find solutions focused on: point anomalies (where each activity is recognized and evaluated individually, then classified as normal or abnormal), collective anomalies (where groups of activities are evaluated together, e.g., the morning routine), and contextual anomalies (where the individual activities are evaluated under a particular context, e.g., a certain medical condition of the monitored person). The typical structure of a system for detecting abnormal behavior according to the Activity Recognition Strategy is shown in Figure 2.  
Figure 1: An overview of the existing strategies for detecting abnormal behavior (based on Refs. [27; 28]). In this approach, the normal behavior is defined as patterns of activities (identified, for example, by clustering, as in Ref. [29]), and the deviations from these patterns are reported as abnormalities. An intuitive example, illustrating the detection of abnormal eating behavior by finding clusters in a bidimensional dataset is shown in Figure 3.  
In the Discriminating Strategy it is assumed that the training set contains data about abnormal events previously recorded, and the detection of abnormalities can be reduced to a binary classification problem. Since annotated datasets that capture abnormal behavior are seldom available, the training for the abnormal class is done with synthetically generated data, as in Ref. [30]. Finally, the Profiling Strategy consists in comparing the observed data with a previously created model of normality. This approach is used in Ref [31], where the normal behavior is modelled in a Bayesian formulation using concepts like'sensor activation likelihood','sensor sequence likelihood', and'sensor event duration likelihood'. Figure 3: An example of defining normal eating habits and finding deviating behaviors by clustering a bidimensional dataset (start time and duration of meals). Abnormal activities A1, A2, A3 are outliers with respect to the clusters. Figure 2: Typical structure of a system for detecting abnormal behavior based on the Activity Recognition Strategy. Another taxonomy of the existing approaches for detecting abnormal human activities is proposed in Ref. [32], as follows:  
* Score-based approaches. These solutions involve periodic assessment of the monitored persons from the perspective of possible health conditions that may affect their ADL routine.