# A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone
## 5 Discussion  
The present study is a continuation of our previous work presented in Ref. [43]. Compared to the previous version, the current solution eliminates the need of a similarity measure to detect deviations from the ADL routine. This reduces the computational load and no longer requires expert knowledge to read the output of the behavior deviation detector. In fact, the entire system requires truly little expert knowledge only for defining the reference interval and assigning the sensors to BMPs. With a proper design of the software user interface, even these tasks can be executed by middle skilled workers, or--in many cases--by the users themselves. We compared the proposed solution with those described in three other studies from the perspective of the criteria defined in Ref. [25]. The evaluation was made by considering the following factors;  
* The complexity of the solution;
* The amount and level of the expert knowledge needed for the implementation and installation;
* The type of sensors used;
* The vulnerability to sensor faults;  
Figure 11: Kasteren House C dataset: An illustration of how the system identified the time slice [00.00 a.m.–00.59 a.m.] from 6 December 2008, as an unusual moment for a snack. (**a**) the activity map for the respective time slice (**b**) plot of the centroid of the activity map vs. the SBC of the centroids for the corresponding time slices of the reference interval. Figure 12: Kasteren House C dataset: An illustration of a false alert, due to a pressure sensor that reports a high level of activity when the user was actually relaxing on a couch. (**a**) the activity map (**b**) the plot of the centroid of the activity map vs. the SBC of the centroids for the corresponding time slices of the reference interval. The availability of training datasets. The results are presented in Table 2.  
Unlike most other solutions for detecting behavior deviations in smart homes, the proposed method is much simpler because it does not use machine learning, and it does not attempt to recognize and classify specific ADLs. This greatly reduces the complexity of the entire system, requiring lower processing power and, by eliminating the need of annotated datasets for the initial training it reduces the effort for installation and setup. The abstraction of the living space makes the proposed solution easily adaptable to almost any dwelling and sensor deployment. The problem of faulty sensors is not trivial in ADL monitoring and can be a major issue for the systems based on machine learning. In our previous work ([43]) we demonstrated that, assuming a certain redundancy in the distribution of sensors, which is quite feasible with low-cost sensors, a system based on this type of activity map can still operate with one or two sensors completely deactivated. This is important because most of the existing solutions are vulnerable to sensor faults. The presence of a mechanism to reduce the number of false alerts is another useful feature that can improve the overall performance of the proposed system. Finally, we have considered the feasibility of the idea of implementing a peer-to-peer (P2P) monitoring system, consisting of a network of smart homes, each equipped with a local monitoring system designed according to the method described in this work. Synthetic information about the activity of the monitored persons is shared in a simple format with the network peers, along with alerts about unusual activity, when detected. If all the peers share information about their behavior patterns, the system allows them to monitor each other's activity and ask for assistance if deviations from the routine are detected. Details on the structure of such a system are presented in Appendix A. Of course, it can be argued that, since the local monitoring system can be designed to work entirely automatically, there is no need to involve the peers in monitoring. It is true.