# Implementation of Context Aware e-Health Environments Based on Social Sensor Networks
## 6 Conclusions  
In this work, the implementation of Context Aware in order to aid in the assistance of end users within the framework of e-Health and m-Health scenarios has been described. A prototype of Social Sensor device has been designed and implemented in order to provide multiple-signal processing capabilities, while exhibiting flexible, low complexity and moderate cost features. The Social Sensor device is based on a Bluetooth transceiver, in which a dedicated microcontroller provides access to several signals, which can provide medical, environmental or behavioral information. In order to analyze the robustness of the system, detailed wireless channel analysis has been performed, providing insight on the behavior of the devices in real complex indoor scenarios, in which large signal variability can severely degrade overall performance. Estimations of received power levels have been compared with experimental results, indicating that received power levels comply with receiver sensitivity levels in the scenario for any potential transceiver location, implying adequate overall system service levels. Initial tests under Living Lab conditions, given by NASISTIC operational requirements have also been performed, showing the feasibility to successfully interchange multiple information within real time regime. Trial runs have shown the feasibility in data acquisition of multiple bio-physical parameters as well as user information, leading to a practical AAL platform. The proposed solution is currently being prepared for large-scale deployment because of the ease in installation and maintenance, positive adoption by end users and adequate results in terms of response time reduction and preventive actions that can be adopted.  
The NASISTIC system is a remote health and behavior monitoring system, providing means to health professionals as well as social services to provide increased quality of living to vulnerable population segments, such as elderly people, and those suffering from some form of disability or mental illness. The difference between NASISTIC and other proposed systems is the integral approach in capturing biomedical signals as well as other signals that are related with user habits, such as usage of lights, heating, electrical appliances, water consumption or open/close indication of doors. In this way, combined action of health specialists and social services can be provided in order to enable higher degrees of autonomy to the users as well as increasing quality of life levels.  
As future steps, complete system validation, integration of additional sensor elements, and the implementation of a distributed system architecture based on cloud integration will be performed.  
The current implementation of NASISTIC system uses a back-end server for information storage. Although this first version is based on a centralized solution, the system is not limited to it. Suitable modifications on back-end element can lead to distributed implementations based on cloud solutions.  
Additionally, complete user experience assessment will be obtained in order to identify usability, leading to further modifications and enhancements.  
The authors wish to acknowledge funding of project NASISTIC, from the Government of Navarra, Spain; Project TEC2013-45585-C2-1-R, funded by the Spanish Ministry of Economy and Competiveness; collaboration and support from Red Cross Navarra; and D2D, AH and ID corporations, in Spain.  
[PERSON], [PERSON], [PERSON] and [PERSON] conducted the characterization of wireless propagation mechanisms in the Living Lab scenarios. [PERSON] and [PERSON] participated in the development of the NASISTIC architecture and back end.  
The authors declare no conflict of interest.