The project performs interdisciplinary research, with three partners from the medical domain and experts in the areas of Obstructive Sleep Apnea (OSA) and next generation of sensors for medical use; and two partners from computing with expertise in mobile systems, sensor data acquisition and processing, signal processing, data analysis, and event detection. The application requirements are determined by the medical experts that will also perform user studies. An extensible data acquisition system will be implemented with smart phones and sensors, like Shimmer motes and the Bitalino sensor set. This system will be used to collect longitudinal data from sleep monitoring at home and in the sleep laboratory (combined with classical polysomnography to annotate the ground truth). Supervised learning (data mining) techniques will be systematically studied for their use to automatically analyze longitudinal data for OSA detection. These studies will use data from the PhysioNet databases (early project phase), and later-on data that has been collected in user studies with the data acquisition system. Furthermore, we investigate the usefulness of supervised and unsupervised learning (data mining) techniques to identify interesting data patterns that might lead to new knowledge in OSA research and to support the design and engineering of the on-line analysis tool. The design of the on-line analysis tool is driven by the goal to enable individuals with limited computing skill to customize and personalize the on-line analysis. To achieve this goal, the following three principles will be strictly applied: use of a declarative approach with Complex Event Processing, using few powerful abstractions of physical and logical sensors, and a fine granular modularization implemented in sensor hierarchies. Furthermore, the team will build tools to quantify the quality of off-line and on-line data analysis results.
Project leader: Vera Goebel
Institution: Institutt for informatikk