Sensor-Mesh-Based System with Application on Sleep Study

  • Maksym GaidukEmail author
  • Bruno Vunderl
  • Ralf Seepold
  • Juan Antonio Ortega
  • Thomas Penzel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)


The process of restoring our body and brain from fatigue is directly depending on the quality of sleep. It can be determined from the report of the sleep study results. Classification of sleep stages is the first step of this study and this includes the measurement of biovital data and its further processing.

In this work, the sleep analysis system is based on a hardware sensor net, namely a grid of 24 pressure sensors, supporting sleep phase recognition. In comparison to the leading standard, which is polysomnography, the proposed approach is a non-invasive system. It recognises respiration and body movement with only one type of low-cost pressure sensors forming a mesh architecture. The nodes implement as a series of pressure sensors connected to a low-power and performant microcontroller. All nodes are connected via a system wide bus with address arbitration. The embedded processor is the mesh network endpoint that enables network configuration, storing and pre-processing of the data, external data access and visualization.

The system was tested by executing experiments recording the sleep of different healthy young subjects. The results obtained have indicated the potential to detect breathing rate and body movement. A major difference of this system in comparison to other approaches is the innovative way to place the sensors under the mattress. This characteristic facilitates the continuous using of the system without any influence on the common sleep process.


Movement detection Respiration rate Sleep study FSR sensor 


  1. 1.
    Hirshkowitz, M., Whiton, K., Albert, S.M., Alessi, C., Bruni, O., DonCarlos, L., Hazen, N., Herman, J., Katz, E.S., Kheirandish-Gozal, L., Neubauer, D.N., O’Donnell, A.E., Oha-yon, M., Peever, J., Rawding, R., Sachdeva, R.C., Setters, B., Vitiello, M.V., Ware, J.C., Adams Hillard, P.J.: National sleep foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health 24(09), 40–43 (2014)Google Scholar
  2. 2.
    Klein, A., Velicu, O.R., Seepold, R.: Sleep stages classification using vital signals recordings. In: Intelligent Solutions in Embedded Systems, pp. 47–50, INSPEC: 15655487 (2015)Google Scholar
  3. 3.
    Rechtschaffen, A., Kales, A.: A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. U.S. Department of Health, Education, and Welfare, pp. 1–12 (1968)Google Scholar
  4. 4.
    Sleep Foundation: What Happens When You Sleep? Accessed 04 Dec 2017
  5. 5.
    Sleep Foundation: How Sleep Affects Brain Function. HealthiNation (2012). Accessed 03 Dec 2017
  6. 6.
    VeryWell: The Four Stages of Sleep (NREM and REM Sleep Cycles). Accessed 28 Nov 2017
  7. 7.
    National Institute of Neurological Disorders and Stroke: Brain Basics: Understanding Sleep. Accessed 05 Dec 2017
  8. 8.
    Sleep Sync: What normally happens during a typical sleep cycle? Accessed 01 Dec 2017
  9. 9.
    Born, J., Wilhelm, I.: System consolidation of memory during sleep. Psychol. Res. 76, 192–203 (2012). ISSN 1430-2772CrossRefGoogle Scholar
  10. 10.
    National Institutes of Health: How Sleep Clears the Brain. Accessed 02 Dec 2017
  11. 11.
    Muzet, A.: Dynamics of body movements in normal sleep. In: Sleep, pp. 232–234 (1988)Google Scholar
  12. 12.
    Nishida, Y., Hori, T., Sato, T., Hirai, S.: The surrounding sensor approach - application to sleep apnea syndrome diagnosis based on image processing. In: IEEE SMC 1999 Conference Proceedings, 1999 IEEE International Conference on Systems, Man, and Cybernetics, vol. 6, pp. 382–388 (1999). ISBN 0-7803-5731-0Google Scholar
  13. 13.
    Blood, M.L., Sack, R.L., Percy, D.C.: A comparison of sleep detection by wrist actigraphy, behavioural response, and polysomnography. Sleep 20(6), 388–395 (1997)Google Scholar
  14. 14.
    Orcioni, S., Conti, M., Gaiduk, M., Seepold, R., Martínez Madrid, N.: A review of health monitoring systems using sensors on beds or cushion. In: Proceedings of International Workshop “Smart Future Living Bodensee”, Konstanz, tbp (2017)Google Scholar
  15. 15.
    Hedner, J., Pillar, G., Pittman, S.D., Zou, D., Grote, L., White, D.P.: A novel adaptive wrist actigraphy algorithm for sleep-wake assessment in sleep apnea patients. Sleep 27(8), 1560–1566 (2004)CrossRefGoogle Scholar
  16. 16.
    Zinkhan, M., Berger, K., Hense, S., Nagel, M., Obst, A., Koch, B., Penzel, T., Fietze, I., Ahrens, W., Young, P., Happe, S., Kantelhardt, J.W., Kluttig, A., Schmidt-Pokrzywniak, A., Pillmann, F., Stang, A.: Agreement of different methods for assessing sleep characteristics: a comparison of two actigraphs, wrist and hip placement, and self-report with polysomnography. Sleep Med. 15, 1107–1114 (2014)CrossRefGoogle Scholar
  17. 17.
    Wohlfahrt, P., Kantelhardt, J.W., Zinkhan, M., Schumann, A.Y., Penzel, T., Fietze, I., Pillmann, F., Stang, A.: Transitions in effective scaling behavior of accelerometric time series across sleep and wake. EPL 103, 68002 (2013)CrossRefGoogle Scholar
  18. 18.
    Velicu, O.R., Martínez Madrid, N., Seepold, R.: Experimental sleep phases monitoring. In: IEEE EMBS International Conference BHI, pp. 625–628 (2016). ISBN 978-1-5090-2455-1Google Scholar
  19. 19.
    Long, X., Fonseca, P., Foussier, J., Haakma, R., Aarts, R.: Sleep and wake classification with actigraphy and respiratory effort using dynamic warping. IEEE J. Biomed. Health Inf. 18, 1272–1284 (2013). ISSN 2168-2194CrossRefGoogle Scholar
  20. 20.
    Pouyan, M.B., Nourani, M., Pompeo, M.: Sleep state classification using pressure sensor mats. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1207–1210 (2015). ISSN 1094-687XGoogle Scholar
  21. 21.
    O’Hare, E., Flanagan, D., Penzel, T., Garcia, C., Frohberg, D., Heneghan, C.: A comparison of radio-frequency biomotion sensors and actigraphy versus polysomnography for the assessment of sleep in normal subjects. Sleep Breath 19, 91–98 (2015)CrossRefGoogle Scholar
  22. 22.
    Wikipedia, the free encyclopaedia: Pressure Sensor. Accessed 29 Nov 2017
  23. 23.
    Yaniger, S.I.: Force sensing resistors: a review of the technology, pp. 666–668 (1991).
  24. 24.
    Pouyan, M.B., Ostadabbas, S., Farshbaf, M., Yousefi, R., Nourani, M., Pompeo, M.D.M.: Continuous eight-posture classification for bed-bound patients. In: 6th International Conference on Biomedical Engineering and Informatics, pp. 121–126 (2013). ISSN 1948-2914Google Scholar
  25. 25.
    Pino, E.J., de la Paz, A.D., Aqueveque, P., Chavez, J.A.P., Moran, A.A.: Contact pressure monitoring device for sleep studies In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4160–4163 (2013). Electronic ISBN 978-1-4577-0216-7Google Scholar
  26. 26.
    Burioka, N., Corńelissen, G., Halberg, F., Kaplan, D.T., Suyama, H., Sako, T.: Approximate entropy of human respiratory movement during eye-closed waking and different sleep stages. CHEST J. 123(1), 80–86 (2003). Scholar
  27. 27.
    Rostig, S., Kantelhardt, J.W., Penzel, T., Cassel, W., Peter, J.H., Vogelmeier, C., Becker, H.F.: Nonrandom variability of respiration during sleep in healthy humans. Sleep 28, 411–417 (2005)CrossRefGoogle Scholar
  28. 28.
    Penzel, T., Kantelhardt, J.W., Bartsch, R.P., Riedl, M., Kraemer, J.F., Wessel, N., Garcia, C., Glos, M., Fietze, I., Schöbel, C.: Modulations of heart rate, ECG, and cardio-respiratory coupling observed in polysomnography. Front. Physiol. 7, 460 (2016). Scholar
  29. 29.
    Penzel, T., Kantelhardt, J.W., Grote, L., Peter, J.H., Bunde, A.: Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Trans. Biomed. Eng. 50, 1143–1151 (2003)CrossRefGoogle Scholar
  30. 30.
    Penzel, T., McNames, J., de Chazal, P., Raymond, B., Murray, A., Moody, G.: Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Med. Biol. Eng. Comput. 40, 402–407 (2002)CrossRefGoogle Scholar
  31. 31.
    Lokavee, S., Puntheeranurak, T., Kerdcharoen, T., Watthanwisuth, N., Tuantranont, A.: Sensor pillow and bed sheet system: unconstrained monitoring of respiration rate and posture movements during sleep. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, pp. 1564–1568 (2012). Electronic ISBN 978-1-4673-1714-6Google Scholar
  32. 32.
    Gaiduk, M., Kuhn, I., Seepold, R., Ortega, J.A., Madrid, N.M.: A sensor grid for pressure and movement detection supporting sleep phase analysis. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017. LNCS, vol. 10209, pp. 596–607. Springer, Cham (2017). Scholar
  33. 33.
    Kim, J.-H., Roberge, R., Powell, J.B., Shafer, A.B., Williams, W.J.: Measurement accuracy of heart rate and respiratory rate during graded exercise and sustained exercise in the heat using the Zephyr BioHarnessTM. Int. J. Sports Med. 34(6), 497–501 (2013). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.HTWG KonstanzKonstanzGermany
  2. 2.Universidad de Sevilla, Avda. Reina Mercedes s/nSevilleSpain
  3. 3.Sleep Medicine Center of CharitéBerlinGermany

Personalised recommendations