Sleep Detection using De-Identified Depth Data

Björn Krüger, Anna Vögele, Marouane Lassiri, Lukas Herwartz, Thomas Terkatz, Andreas Weber, Carmen Garcia, Ingo Fietze und Thomas Penzel
In: Journal of Mobile Multimedia (Dez. 2014), 10:3&4(327-342)
 

Abstract

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, confidentiality 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 offering 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-identified data sets.

Stichwörter: polysomnography, privacy protection, self-monitoring, sleep

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Bibtex

@ARTICLE{krueger2014c,
    author = {Kr{\"u}ger, Bj{\"o}rn and V{\"o}gele, Anna and Lassiri, Marouane and Herwartz, Lukas and Terkatz, Thomas and
              Weber, Andreas and Garcia, Carmen and Fietze, Ingo and Penzel, Thomas},
     pages = {327--342},
     title = {Sleep Detection using De-Identified Depth Data},
   journal = {Journal of Mobile Multimedia},
    volume = {10},
    number = {3{\&}4},
      year = {2014},
     month = dec,
  keywords = {polysomnography, privacy protection, self-monitoring, sleep},
  abstract = {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, confidentiality 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 offering 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-identified data sets.}
}