Sleep Detection using a Depth Camera

Björn Krüger, Anna Vögele, Lukas Herwartz, Thomas Terkatz, Andreas Weber, Carmen Garcia, Ingo Fietze und Thomas Penzel
In: Lecture Notes in Computer Science, ICCSA 2014, Part I (Juni 2014), LNCS 8579(824-835)
 

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 with a Kinect device, thus making the method non-invasive independent of immediate physical contact to subjects. The results of a study carried out as proof of concept are discussed and compared with the polysomnography-based gold standard of sleep analysis.

Stichwörter: polysomnography, self-monitoring, sleep

Bilder

Bibtex

@ARTICLE{krueger2014b,
     author = {Kr{\"u}ger, Bj{\"o}rn and V{\"o}gele, Anna and Herwartz, Lukas and Terkatz, Thomas and Weber, Andreas and
               Garcia, Carmen and Fietze, Ingo and Penzel, Thomas},
      pages = {824--835},
      title = {Sleep Detection using a Depth Camera},
    journal = {Lecture Notes in Computer Science, ICCSA 2014, Part I},
  booktitle = {International Conference on Computational Science and Its Applications (ICCSA) [accepted for
               publication]},
     volume = {LNCS 8579},
       year = {2014},
      month = jun,
   location = {Minho, Guimaraes, Portugal},
   keywords = {polysomnography, 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 with a Kinect device, thus making the method
               non-invasive independent of immediate physical contact to subjects. The results of a study carried
               out as proof of concept are discussed and compared with the polysomnography-based gold standard of
               sleep analysis.}
}