One of the major problems in the development of information and communication technologies for older adults is user acceptance. Here we describe the results of focus group discussions that were conducted with older adults and their relatives to guide the development of assistive devices for fall detection and fall prevention. The aim was to determine the ergonomic and functional requirements of such devices and to include these requirements in a user-centered development process. A semi-structured interview format based on an interview guide was used to conduct three focus group discussions with 22 participants. The average age was 75 years in the first group, 68 years in the second group and 50 years in the third group (relatives). Overall, participants considered a fall prediction system to be as important as a fall detection system. Although the ambient, unobtrusive character of the optical sensor system was appreciated, wearable inertial sensors were preferred because of their wide range of use, which provides higher levels of security. Security and mobility were two major reasons for people at risk of falling to buy a wearable and/or optical fall prediction and fall detection device. Design specifications should include a wearable, non-stigmatising sensor at the user's wrist, with an emergency option in case of falling.

Current fall detection technologies work well under laboratory conditions but it is still problematic to produce reliable results when these technologies are applied to real life conditions. Acceptance towards the sensors decreased after study participation although the system was generally perceived as useful or very useful.

The study shows that there is a considerably large difference between fall-detector evaluation studies in domestic environments and in laboratory trials.

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Many societies across the world are confronted with demographic changes, usually related to increased life expectancy and, often, relatively low birth rates. Information and communication technologies (ICT) may contribute to adequately support senior citizens in aging societies with respect to quality of life and quality and efficiency of health care processes. For investigating and for providing answers on whether new information and communication technologies can contribute to keeping, or even improving quality of life, health and self-sufficiency in ageing societies through new ways of living and new forms of care, the Lower Saxony Research Network Design of Environments for Ageing (GAL) had been established as a five years research project, running from 2008 to 2013. Ambient-assisted living (AAL) technologies in personal and home environments were especially important. In this article we report on the GAL project, and present some of its major outcomes after five years of research. We report on major challenges and lessons learned in running and organizing such a large, inter- and multidisciplinary project and discuss GAL in the context of related research projects. With respect to research outcomes, we have, for example, learned new knowledge about multimodal and speech-based human-machine-interaction mechanisms for persons with functional restrictions, and identified new methods and developed new algorithms for identifying activities of daily life and detecting acute events, particularly falls. A total of 79 apartments of senior citizens had been equipped with specific "GAL technology", providing new insights into the use of sensor data for smart homes. Major challenges we had to face were to deal constructively with GAL's highly inter- and multidisciplinary aspects, with respect to research into GAL's application scenarios, shifting from theory and lab experimentation to field tests, and the complexity of organizing and, in our view, successfully managing such a large project. Overall it can be stated that, from our point of view, the GAL research network has been run successfully and has achieved its major research objectives. Since we now know much more on how and where to use AAL technologies for new environments of living and new forms of care, a future focus for research can now be outlined for systematically planned studies, scientifically exploring the benefits of AAL technologies for senior citizens, in particular with respect to quality of life and the quality and efficiency of health care.

In this article, the design of a system for the ambient, unobtrusive and automatic monitoring of Activities of Daily Living (ADL) is described. In the context of the growing imbalance between (potentially young) caregivers and (most often older) people receiving care, technical monitoring systems may help to organise care more efficiently and to identify degrading abilities very early to trigger preventive measures. To improve the acceptance of the system described in this article, the selection process of the sensors to be integrated into the flat or to be worn by the older people has been steered by the results of focus group interviews with older people, their relatives and professional caregivers. The interviews revealed that these people would in general accept such systems, but security, mobility and communication aspects have to be clearly and appropriately addressed. In an experimental study the recognition rate of the activity 'preparation and intake of food or beverages' has been measured with two age groups (6 subjects, age between 25 and 40/mean 30 years and 5 subjects, age between 72 and 84/mean 75.3 years). The food preparation was detected with a sensitivity of 74.7% and a specificity of 84.2% using a vision sensor.

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In this paper we propose a novel part-based approach to scene understanding, that allows us to infer the properties of traffic scenes, such as location and geometry of lanes and roads. Lanes and roads are parts of our undirected graphical model in which nodes represent parts or sub-parts of scenes and edges represent spatial constraints. Spatial constraints are statistically formulated and allow us to take advantage of low-level relations as well as high-level contextual information. The estimation of scene properties is formulated as an inference problem, which is solved using non-parametric belief propagation. Inferring about high-level scene properties, while relying on error-prone sensory cues is challenging and computational expensive. Therefore, we introduced a novel depth-first message passing scheme. This scheme is applied to several challenging real world scenarios showing robust results and real-time performance.