@article{SeepoldGaidukOrtegaetal.,
  author    = {Seepold, Ralf and Gaiduk, Maksym and Ortega, Juan Antonio and Conti, Massimo and Orcioni, Simone and Mart{\´i}nez Madrid, Natividad},
  title     = {Home hospital e-health centers for barrier-free and cross-border telemedicine},
  series = {Intelligent Decision Technologies 2019, Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019) - Volume 1 ; (Smart Innovation, Systems and Technologies ; 143)},
  edition   = {First online: 2 June 2019},
  publisher = {Springer},
  address   = {Singapore},
  isbn      = {978-981-13-8302-1},
  pages     = {307 -- 316},
  abstract  = {The goal of the presented project is to develop the concept of home ehealth centers for barrier-free and cross-border telemedicine. AAL technologies are already present on the market but there is still a gap to close until they can be used for ordinary patient needs. The general idea needs to be accompanied by new services, which should be brought together in order to provide a full coverage of service for the users. Sleep and stress were chosen as predominant diseases for a detailed study within this project because of their widespread influence in the population. The executed scientific study of available home devices analyzing sleep has provided the necessary to select appropriate devices. The first choice for the project implementation is the device EMFIT QS+. This equipment provides a part of a complete system that a home telemedical hospital can provide at a level of precision and communication with internal and/or external health services.},
  language  = {en}
}
@article{SandybekovGrabowGaiduketal.,
  author    = {Sandybekov, Maksim and Grabow, Clemens and Gaiduk, Maksym and Seepold, Ralf},
  title     = {Posture tracking using a machine learning algorithm for a home AAL environment},
  series = {Intelligent Decision Technologies 2019, Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019) - Volume 2 ; (Smart Innovation, Systems and Technologies ; 143)},
  edition   = {First online: 2 June 2019},
  publisher = {Springer},
  address   = {Singapore},
  isbn      = {978-981-13-8302-1},
  pages     = {337 -- 347},
  abstract  = {The number of home office workers sitting for many hours is increasing. The sensor chair is tracking users' sitting behavior which the help of pressure sensors and tries to avoid wrong postures which may cause diseases. The system provides live monitoring of the pressure distribution via web interface, as well as sitting posture prediction in real time. Posture analysis is realized through machine learning algorithm using a decision tree classifier that is compared to a random forest. Data acquisition and aggregation for the learning process happens with a mobile app adding users biometrical data and the taken sitting posture as label. The sensor chair is able to differentiate between an arched back, a neutral posture or a laid back position taken on the chair. The classifier achieves an accuracy of 97.4\% on our test set and is comparable to the performance of the random forest with 98.9\%.},
  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}
}
@article{GaidukSeepoldMartinezMadridetal.,
  author    = {Gaiduk, Maksym and Seepold, Ralf and Mart{\´i}nez Madrid, Natividad and Ortega, Juan Antonio and Penzel, Thomas},
  title     = {Non-invasives System f{\"u}r die kontinuierliche Schlafanalyse},
  series = {6. Ambient Medicine® Forum „Assistive Technik f{\"u}r selbstbestimmtes Wohnen", 19.-20. Februar 2019, Hochschule Kempten, Tagungsband},
  publisher = {Cuvillier Verlag},
  address   = {G{\"o}ttingen},
  isbn      = {978-3-7369-9961-9},
  pages     = {23 -- 28},
  abstract  = {Die Erholung unseres K{\"o}rpers und Gehirns von M{\"u}digkeit ist direkt abh{\"a}ngig von der Qualit{\"a}t des Schlafes, die aus den Ergebnissen einer Schlafstudie ermittelt werden kann. Die Klassifizierung der Schlafstadien ist der erste Schritt dieser Studie und beinhaltet die Messung von Biovitaldaten und deren weitere Verarbeitung. Das non-invasive Schlafanalyse-System basiert auf einem Hardware-Sensornetz aus 24 Drucksensoren, das die Schlafphasenerkennung erm{\"o}glicht. Die Drucksensoren sind mit einem energieeffizienten Mikrocontroller {\"u}ber einen systemweiten Bus mit Adressarbitrierung verbunden. Ein wesentlicher Unterschied dieses Systems im Vergleich zu anderen Ans{\"a}tzen ist die innovative Art, die Sensoren unter der Matratze zu platzieren. Diese Eigenschaft erleichtert die kontinuierliche Nutzung des Systems ohne f{\"u}hlbaren Einfluss auf das gewohnte Bett. Das System wurde getestet, indem Experimente durchgef{\"u}hrt wurden, die den Schlaf verschiedener gesunder junger Personen aufzeichneten. Die ersten Ergebnisse weisen auf das Potenzial hin, nicht nur Atemfrequenz und K{\"o}rperbewegung, sondern auch Herzfrequenz zu erfassen.},
  language  = {de}
}
@article{GaidukPenzelOrtegaetal.,
  author    = {Gaiduk, Maksym and Penzel, Thomas and Ortega, Juan Antonio and Seepold, Ralf},
  title     = {Automatic sleep stages classification using respiratory, heart rate and movement signals},
  series = {Physiological Measurement},
  volume    = {39},
  number    = {12},
  issn      = {0967-3334},
  doi       = {10.1088/1361-6579/aaf5d4},
  pages     = {14},
  abstract  = {Objective: This paper presents an algorithm for non-invasive sleep stage identification using respiratory, heart rate and movement signals. The algorithm is part of a system suitable for long-term monitoring in a home environment, which should support experts analysing sleep. Approach: As there is a strong correlation between bio-vital signals and sleep stages, multinomial logistic regression was chosen for categorical distribution of sleep stages. Several derived parameters of three signals (respiratory, heart rate and movement) are input for the proposed method. Sleep recordings of five subjects were used for the training of a machine learning model and 30 overnight recordings collected from 30 individuals with about 27 000 epochs of 30 s intervals each were evaluated. Main results: The achieved rate of accuracy is 72\% for Wake, NREM, REM (with Cohen's kappa value 0.67) and 58\% for Wake, Light (N1 and N2), Deep (N3) and REM stages (Cohen's kappa is 0.50). Our approach has confirmed the potential of this method and disclosed several ways for its improvement. Significance: The results indicate that respiratory, heart rate and movement signals can be used for sleep studies with a reasonable level of accuracy. These inputs can be obtained in a non-invasive way applying it in a home environment. The proposed system introduces a convenient approach for a long-term monitoring system which could support sleep laboratories. The algorithm which was developed allows for an easy adjustment of input parameters that depend on available signals and for this reason could also be used with various hardware systems.},
  language  = {en}
}