Context-aware services for ambient assisted living: A case-study

Abstract

Ambient assisted living is a paradigm that promotes independency in the old age with the support of advanced technologies. Ambient home care systems (AHCS) are specially design for this purpose; they aim at minimizing the potential risks that living alone may suppose for an elder, thanks to their capability of gathering data of the user, inferring information about his activity and state, and taking decisions on it. In this paper, we present a number of context-aware services (heart rate monitoring, medication prompting, generation of agenda reminders, weather alerts, emergency notifications, etc.) for the elder and his caregivers. They run on the top of an AHCS, which collects data from a network of environmental, health and physical sensors. The AHCS follows a layered fusion architecture, formed by an in-home developed context acquisition framework and a context manager (customized on the Context Toolkit) that holds the inference and reasoning functionalities. On the deployed prototype, we analyze the suitability of the selected technical approach for ambient assisted living applications.

Application of Cognitive Rehabilitation Theory to the Development of Smart Prompting Technologies

“…Integrating multiple types of contextual information to provide more flexible and adaptive assistance to individuals is supported by CRT. Hristova and colleagues [65] later used the Context Toolkit to build a prompting application for ambient assisted living to support heart rate monitoring, medication prompting, generation of agenda reminders, weather alerts, emergency notification, etc. The system was, however, limited by a lack of data privacy, security, and robust conflict reasoning mechanisms.…”

Indoor Position System Based on BitCloud Stack for Ambient Living and Smart Buildings

“…They are used in Ambient Living [1][2][3][4] or Smart building [5][6][7][8][9] researching fields for solving data acquisition process, depending on its applications, it can be used ambient or user sensors and actuators for making decisions. The knowledge of the subject's position it is very useful in these kind of systems because depending on it the decisions to be made are different, this paper present an indoor position solution for doing so.…”

Learning to Identify Users and Predict Their Destination in a Robotic Guidance Application

“…However, for the tests on the 21th day, a real robot was used. 2 In a second phase, the real robot was used by each of the user, the robot having no preliminary knowledge about the current user. At each time step, the system computes the probability distribution over the user, the goal, and the action with, respectively, equations 1, 2, and 3.…”

“…environments where sensors have been installed in order to monitor the activity of human beings. The accumulated data are used to train algorithms which later serves to determine human activities, supervise the user's condition and medication, or detect anomalies [2,4,6,13]. Some researches try to further determine the attributes (coffee drinker, smoker) of multiple users based on their location [3,5].…”