SLEEP DETECTION USING DE-IDENTIFIED DEPTH DATA
Keywords:
sleep, self-monitoring, polysomnography, privacy protectionAbstract
The work at hand presents a method to assess the quality of human sleep within a non-laboratory environment. The monitoring of patients is performed by means of a Kinect device. This results in a non-invasive method which is independent of immediate physical contact to subjects. The results of a study which was carried out as proof of concept are discussed and compared with the polysomnography-based gold standard of sleep analysis. When medical data are concerned, condentiality is always an issue. This is no less important when monitoring people in their own homes, especially when they are in a situation as vulnerable as sleep. To meet the upcoming challenge of protecting people's privacy while still oering analyses of their data we introduce a blurring method to the acquired data and evaluate the use of our sleep detection test on such de-identied data sets.
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