Projektstart: 1. September 2014
Projektende: 31. August 2016
Projektlaufzeit: 24 Monate

Prof.(FH) Dr. Ulrich Reimer, FH St.Gallen (Project Leader)
Prof.(FH) Dr. Ralf E.D. Seepold, HTWG Konstanz
Prof.(FH) Dr. Hans Vollbrecht, FH Vorarlberg

FH St.Gallen
HTWG Konstanz
FH Vorarlberg
Biovotion AG
Schlafmedizin Klinik Barmelweid


Detaillierte Projektbeschreibung:
Sleep disorders are widely distributed and often they are accompanied with chronic health problems like diabetes, hypertension, cardiovascular and psychiatric diseases (like depression). For example, sleep apnea, a sleep disorder provoking frequent interrupts on breathing, impacts 4% of the male and 2% of the female population. The majority of studies about sleep disorders are based on questionnaires capturing information about sleep, living habits, actigraphy, polysomnography or measurements taken in a laboratory.

Modern developments like smart watches in mobile health care offer the possibility to capture behavior and sleep patterns during and over the day/night for a longer period and this will support a medical professional in their diagnosis. Telemedicine will support monitoring the treatment of sleep disorders over time. The objective of the project SmartSleep is to analyze a huge source of biometric data, to detect patterns and to derive relationships between a person’s behavior and sleep disorders. In order to capture the data, mobile sensors connected 24 hours per day will be used. Besides the movement, these sensors will detect additional parameters as input to clinical long-term studies. One task is to provide mechanisms pre-processing the data stream before pushing it to the backbone servers. So, even the patient may receive feedback in real-time. A second task is to provide data analysis components beyond current state of the art systems. The goal is to close the gap in existing data-mining algorithms by extending them with scenarios relevant for sleep disorders.

The overall project result will provide a remarkable advancement over a purely statistical relationship analysis within a population and it offers a perspective to achieve a system efficiently customizable to individual needs of a patient without strong intervention.

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