@inproceedings{GaidukSeepoldMart{\´i}nez Madridetal.2022,
  author    = {Gaiduk, Maksym and Seepold, Ralf and Mart{\´i}nez Madrid, Natividad and Penzel, Thomas and Weber, Lucas and Conti, Massimo and Orcioni, Simone and Ortega, Juan Antonio},
  title     = {Evaluating body movement and breathing signals for identification of sleep/wake states},
  booktitle = {Applications in electronics pervading industry, environment and society : APPLEPIES 2021. - (Lecture Notes in Electrical Engineering ; 866 )},
  editor    = {Saponara, Sergio and De Gloria, Alessandro},
  isbn      = {978-3-030-95497-0},
  doi       = {10.1007/978-3-030-95498-7_29},
  institution = {Informatik},
  pages     = {206 -- 211},
  year      = {2022},
  abstract  = {Recognition of sleep and wake states is one of the relevant parts of sleep analysis. Performing this measurement in a contactless way increases comfort for the users. We present an approach evaluating only movement and respiratory signals to achieve recognition, which can be measured non-obtrusively. The algorithm is based on multinomial logistic regression and analyses features extracted out of mentioned above signals. These features were identified and developed after performing fundamental research on characteristics of vital signals during sleep. The achieved accuracy of 87\% with the Cohen's kappa of 0.40 demonstrates the appropriateness of a chosen method and encourages continuing research on this topic.},
  language  = {en}
}