@inproceedings{GaidukSeepoldRodriguezdeTrujillo,
  author    = {Gaiduk, Maksym and Seepold, Ralf and Rodr{\´i}guez de Trujillo, Eva},
  title     = {Sleep position recognition in home environment},
  series = {Fachtagung "Smart Day 2018", Veranstalter: Smart Home \& Living Baden-W{\"u}rttemberg e.V., 7. Nov. 2018, Villingen-Schwenningen},
  url       = {http://nbn-resolving.de/https://www.shl-bw.de/fileadmin/documents/SmartDay/Tagungsband_Smart_Day_2018.pdf},
  pages     = {21 -- 24},
  abstract  = {This paper presents a bed system able to analyze a person's movement, breathing and recognize the positions that the subject is lying on the bed during the night without any additional physical contact. The measurements are performed with sensors placed between the mattress and the bed-frame. An Intel Edison board was used as an endpoint that served as a communication node from the mesh network to external service. Two nodes and Intel Edison are attached to the bottom of the bed frame and they are connected to the sensors. First test results have indicated the potential of the proposed approach for the recognition of sleep positions with 83\% of correct recognized positions.},
  language  = {en}
}
@article{RodriguezdeTrujilloSeepoldGaiduk,
  author    = {Rodr{\´i}guez de Trujillo, Eva and Seepold, Ralf and Gaiduk, Maksym},
  title     = {Position recognition algorithm using a two-stage pattern classification set applied in sleep tracking},
  series = {Procedia Computer Science},
  volume    = {126},
  issn      = {1877-0509},
  doi       = {10.1016/j.procs.2018.08.095},
  pages     = {1819 -- 1827},
  abstract  = {The overall goal of this work is to detect and analyze a person's movement, breathing and heart rate during sleep in a common bed overnight without any additional physical contact. The measurement is performed with the help of sensors placed between the mattress and the frame. A two-stage pattern classification algorithm based has been implemented that applies statistics analysis to recognize the position of patients. The system is implemented in a sensors-network, hosting several nodes and communication end-points to support quick and efficient classification. The overall tests show convincing results for the position recognition and a reasonable overlap in matching.},
  language  = {en}
}