Information and communication technologies for promoting and sustaining quality of life, health and self-sufficiency in ageing societies--outcomes of the Lower Saxony Research Network Design of Environments for Ageing (GAL).

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.

Multimodal activity monitoring for home rehabilitation of geriatric fracture patients--feasibility and acceptance of sensor systems in the GAL-NATARS study.

BACKGROUND: Demographic change will lead to a diminishing care workforce faced with rising numbers of older persons in need of care, suggesting meaningful use of health-enabling technologies, and home monitoring in particular, to contribute to supporting both the carers and the persons in need. OBJECTIVES: We present and discuss the GAL-NATARS study design along with first results regarding technical feasibility of long-term home monitoring and acceptance of different sensor modalities. METHODS: Fourteen geriatric participants with mobility-impairing fractures were recruited in three geriatric clinics. Following inpatient geriatric rehabilitation, their homes were equipped with ambient sensor components for three months. Additionally, a wearable accelerometer was employed. Technical feasibility was assessed by system and component downtimes, technology acceptance by face-to-face interviews. RESULTS: The overall system downtime was 6%, effected by two single events, but not by software failures. Technology acceptance was rated very high by all participants at the end of the monitoring periods, and no interference with their social lives was reported. DISCUSSION AND CONCLUSIONS: Home-monitoring technologies were well-accepted by our participants. The information content of the data still needs to be evaluated with regard to clinical outcome parameters as well as the effect on the quality of life before recommending large-scale implementations.

A clinical study to assess fall risk using a single waist accelerometer.

Falls have various causes and are often associated with mobility impairments. Preventive steps to avoid falls may be initiated, if an increasing fall risk could be detected in time. The objective of this article is to identify an automated sensor-based method to determine fall risk of patients based on objectively measured gait parameters. One hundred fifty-one healthy subjects and 90 subjects at risk of falling were measured during a Timed 'Up & Go' test with a single triaxial acceleration sensor worn on a waist belt. The fall risk was assessed using the STRATIFY score. A decision tree induction algorithm was used to distinguish between subjects with high and low risk using the determined gait parameters. The results of the risk classification produce an overall accuracy of 90.4% in relation to STRATIFY score. The sensitivity amount to 89.4%, the specificity to 91.0% and the reliability parameter kappa equals 0.79. The method presented is able to distinguish between subjects with high and low fall risk. It is unobtrusive and therefore may be applied over extended time periods. A subsequent study is needed to confirm the model's suitability for data recorded in patients' everyday lives.

Assessing elderly persons' fall risk using spectral analysis on accelerometric data--a clinical evaluation study.

Falls are among the leading causes for morbidity, mortality and lasting functional disability in the elderly population. Several studies have shown the applicability of accelerometry to detect persons with a high fall risk. Most of these studies have been conducted under laboratory settings and without clear definition of 'fall risk' reference measures. The aim of our work is to provide a simple unsupervised method to assess the fall risk of elderly persons as measured by reference clinical fall risk assessment scores. Our method uses parameters computed by spectral analysis on triaxial accelerometer data recorded in a clinical setting, and is evaluated using simple logistic regression classifier models with reference to three clinical reference scores. The overall prediction accuracy of the models ranges from 65.5-89.1%, with sensitivity and specificity between 78.5-99% and 15.4-60.4%, respectively. Our results show that our simple method can be used to detect persons with a high fall risk with a fair to good predictive accuracy when tested against common clinical reference scores. Our parameters are independent of specific test procedures and therefore are suited for use in an unsupervised setting. Our future research will include the evaluation of our method in a large prospective study.