Behavior and Capability Based Access Control Model for Personalized TeleHealthCare Assistance

With the growing proportion of dependant people (ageing, disabled users), Tele-assistance and Tele-monitoring platforms will play a significant role to deliver an efficient and less-costly remote care in their assistive living environments. Sensor based technology would greatly contribute to get valuable information which should help to provide personalized access to the services available within their living spaces. However, current access control models remain unsuitable due to the lack of completeness, flexibility and adaptability to the user profile. In this paper, we propose a new access control model based on the user capabilities and behavior. This model is evaluated using the data sensed from our tele-monitoring platform in order to assist automatically the dependent people according to the occurred situation. The design of our model is a dynamic ontology and evolving security policy according to the access rules that are used in the inference engine to provide the right service according to the user’s needs. Our security policy reacts according to the detected distress situation derived from the data combination of both the wearable devices and the pervasive sensors. The security policy is managed through the classification and reasoning process. Our classification process aims to extract the behavior patterns which are obtained by mining the data set issued from our Tele-monitoring platform according to the discriminating attributes: fall, posture, movement, time, user presence, pulse and emergency call. Our reasoning process aims to explore the recognized context and the extracted behavior patterns which set up the rule engine to infer the right decision security policy.