@inproceedings{DatkoScherzVelicuetal.2016,
  author    = {Datko, Patrick and Scherz, Wilhelm Daniel and Velicu, Oana Ramona and Seepold, Ralf and Mart{\´i}nez Madrid, Natividad},
  title     = {Personal recommendation system for improving sleep quality},
  booktitle = {Intelligent decision technologies 2016 : proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016). - part I. - (Smart innovation, systems and technologies ; 56)},
  editor    = {Czarnowski, Ireneusz},
  isbn      = {978-3-319-39630-9},
  doi       = {10.1007/978-3-319-39630-9_34},
  institution = {Informatik},
  pages     = {405 -- 412},
  year      = {2016},
  abstract  = {Sleep is an important aspect in life of every human being. The average sleep duration for an adult is approximately 7 h per day. Sleep is necessary to regenerate physical and psychological state of a human. A bad sleep quality has a major impact on the health status and can lead to different diseases. In this paper an approach will be presented, which uses a long-term monitoring of vital data gathered by a body sensor during the day and the night supported by mobile application connected to an analyzing system, to estimate sleep quality of its user as well as give recommendations to improve it in real-time. Actimetry and historical data will be used to improve the individual recommendations, based on common techniques used in the area of machine learning and big data analysis.},
  language  = {en}
}