The Lower Saxony research network design of environments for ageing: towards interdisciplinary research on information and communication technologies in ageing societies.

Worldwide, ageing societies are bringing challenges for independent living and healthcare. Health-enabling technologies for pervasive healthcare and sensor-enhanced health information systems offer new opportunities for care. In order to identify, implement and assess such new information and communication technologies (ICT) the 'Lower Saxony Research Network Design of Environments for Ageing' (GAL) has been launched in 2008 as interdisciplinary research project. In this publication, we inform about the goals and structure of GAL, including first outcomes, as well as to discuss the potentials and possible barriers of such highly interdisciplinary research projects in the field of health-enabling technologies for pervasive healthcare. Although GAL's high interdisciplinarity at the beginning slowed down the speed of research progress, we can now work on problems, which can hardly be solved by one or few disciplines alone. Interdisciplinary research projects on ICT in ageing societies are needed and recommended.

Supporting rehabilitation training of COPD patients through multivariate sensor-based monitoring and autonomous control using a Bayesian network: prototype and results of a feasibility study.

Repeated endurance training - supervised by an expert - is one of the most effective rehabilitation methods for patients with chronic obstructive pulmonary disease (COPD) to improve physical function. Monitoring of vital signs in combination with an automatic intelligent training control and emergency detection facilitates supervised training without the physical presence of an expert as well as training optimisation through individualisation. The aim of this study is the development of a suitable analysis and control method for this purpose. Healthy volunteers and patients with COPD were equipped with body sensors during ergometer training to enable measuring their vital signs continuously. Depending on these values, the exercise load of the ergometer was controlled automatically using a Bayesian network. The network, trained with expert knowledge and training data, is embedded in our system by using Java application programming interface. Extensive tests in a laboratory setting have proved safe usage of our prototype. In a case study, evaluation during training sessions with patients with COPD took place. Due to the automatic control the patients' vital signs ranged inside the predefined optimal thresholds for at least 95% of the time. Furthermore, our results suggest an increase of the training efficiency compared with the conventional method (constant exercise load).