The OCarePlatform : a context-aware system to support independent living

Background: Currently, healthcare services, such as institutional care facilities, are burdened with an increasing number of elderly people and individuals with chronic illnesses and a decreasing number of competent caregivers. Objectives: To relieve the burden on healthcare services, independent living at home could be facilitated, by offering individuals and their (in)formal caregivers support in their daily care and needs. With the rise of pervasive healthcare, new information technology solutions can assist elderly people ("residents") and their caregivers to allow residents to live independently for as long as possible. Methods: To this end, the OCarePlatform system was designed. This semantic, data-driven and cloud based back-end system facilitates independent living by offering information and knowledge-based services to the resident and his/her (in)formal caregivers. Data and context information are gathered to realize context-aware and personalized services and to support residents in meeting their daily needs. This body of data, originating from heterogeneous data and information sources, is sent to personalized services, where is fused, thus creating an overview of the resident's current situation. Results: The architecture of the OCarePlatform is proposed, which is based on a service-oriented approach, together with its different components and their interactions. The implementation details are presented, together with a running example. A scalability and performance study of the OCarePlatform was performed. The results indicate that the OCarePlatform is able to support a realistic working environment and respond to a trigger in less than 5 seconds. The system is highly dependent on the allocated memory. Conclusion: The data-driven character of the OCarePlatform facilitates easy plug-in of new functionality, enabling the design of personalized, context-aware services. The OCarePlatform leads to better support for elderly people and individuals with chronic illnesses, who live independently. (C) 2016 Elsevier Ireland Ltd. All rights reserved

Familiarization through Ambient Images Alone

The term “ambient images” has begun to show up in much of the current literature on facial recognition. Ambient images refer to naturally occurring views of a face that captures the idiosyncratic ways in which a target face may vary (Ritchie & Burton, 2017). Much of the literature on ambient images have concluded that exposing people to ambient images of a target face can lead to improved facial recognition for that target face. Some studies have even suggested that familiarity is the result of increased exposure to ambient images of a target face (Burton, Kramer, Ritchie, & Jenkins, 2016). The current study extended the literature on ambient images. Using the face sorting paradigm from Jenkins, White, Van Montfort, and Burton (2011), the current study served three purposes. First, this study captured whether there was an incremental benefit in showing ambient images. Particularly, we observed whether performance improved as participants were shown a low, medium, or high number of ambient images. Next, this study attempted to provide a strong enough manipulation that participant would be able to perform the face sorting task perfectly, after being exposed to a high number (45 total) of ambient images. Lastly, this study introduced time data as a measure of face familiarity. The results found support for one aim of this study and partial support for another aim of this study. Time data were found to be an effective quantitative measure of familiarity. Also, there was some evidence of an incremental benefit of ambient images, but that benefit disappeared after viewing around 15 unique exemplar presentations of a novel identity’s face. Lastly, exposing participants to 45 ambient images alone did not cause them to reach perfect performance. The paper concludes with a discussion on the need to extend past ambient images to understand how to best mimic natural familiarity in a lab setting

In the Truman show: generating dynamic scenarios in a driving simulator

All the devices, animals, and people make their decisions based on what you're doing, but you don't know it or even notice it. Your world is that of Truman Burbank, from the 1998 movie The Truman Show. With this idea in mind, we've taken the movie metaphor to implement a prototype simulation system where the user steps into Truman's shoes. The set of our "movie" is a driving simulator, and the user is learning to drive a car. During the driving lessons, users drive in a virtual world that lets them experience all kinds of traffic scenarios. The system generates the scenarios with the student as the focal point, and the other traffic entities respond to the student's behavior, without the student noticing. To control the traffic scenarios and make them more effective, our prototype employs an agent-based framework. In this framework, each entity in the simulator is an actor agent playing a role. The prototype also includes a hierarchy of directors that directs the main action and the behind-the-scenes activity. The advantage of the movie metaphor is that it helps separate scenario description from scenario playing. The agents can read their required information from a script and perform their actions based on that information. Using this framework lets us build software that's extensible, maintainable, and easy to understan

The intelligent room for elderly care

Daily life assistance for elderly is one of the most promising and interesting scenarios for advanced technologies in the present and near future. Improving the quality of life of elderly is also some of the first priorities in modern countries and societies where the percentage of elder people is rapidly increasing due mainly to great improvements in medicine during the last decades. In this paper, we present an overview of our informationally structured room that supports daily life activities of elderly. Our environment contains different distributed sensors including a floor sensing system and several intelligent cabinets. Sensor information is sent to a centralized management system which processes the data and makes it available to a service robot which assists the people in the room. One important restriction in our intelligent environment is to maintain a small number of sensors to avoid interfering with the daily activities of people and to reduce as much as possible the invasion of their privacy. In addition we discuss some experiments using our real environment and robot