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Evaluation of a Contactless Accelerometer Sensor System for Heart Rate Monitoring During Sleep

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Applications in Electronics Pervading Industry, Environment and Society (ApplePies 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1110))

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Abstract

The monitoring of a patient's heart rate (HR) is critical in the diagnosis of diseases. In the detection of sleep disorders, it also plays an important role. Several techniques have been proposed, including using sensors to record physiological signals that are automatically examined and analysed. This work aims to evaluate using a contactless HR monitoring system based on an accelerometer sensor during sleep. For this purpose, the oscillations caused by chest movements during heart contractions are recorded by an installation mounted under the bed mattress. The processing algorithm presented in this paper filters the signals and determines the HR. As a result, an average error of about 5 bpm has been documented, i.e., the system can be considered to be used for the forecasted domain.

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References

  1. Conti M, Orcioni S, Martínez Madrid N, Gaiduk M, Seepold R (2018) A review of health monitoring systems using sensors on bed or cushion. In: Rojas I, Ortuño F (eds) IWBBIO 2018. Lecture Notes in Computer Science (2018), vol 10814. Springer, Cham. https://doi.org/10.1007/978-3-319-78759-6_32

  2. Rodríguez de Trujillo E, Seepold R, Gaiduk M, Martínez Madrid N, Orcioni S, Conti M (2019) Embedded system to recognize movement and breathing in assisted living environments. In: Saponara S, De Gloria A (eds) Applications in electronics pervading industry, environment and society (2019). ApplePies 2018. Lecture Notes in Electrical Engineering, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-030-11973-7_46

  3. Stamatakis E, de Rezende LFM, Rey-López JP (2018) Sedentary behaviour and cardiovascular disease. In: Leitzmann M, Jochem C, Schmid D (eds) Sedentary behaviour epidemiology. Springer Series on Epidemiology and Public Health. Springer, Cham. https://doi.org/10.1007/978-3-319-61552-3_9

  4. Gaiduk M, Serrano Alarcón Á, Seepold R, Martínez Madrid N (2023) Current status and prospects of automatic sleep stages scoring: review. BMEL. https://doi.org/10.1007/s13534-023-00299-3

    Article  Google Scholar 

  5. Caples SM et al (2021) Use of polysomnography and home sleep apnea tests for the longitudinal management of obstructive sleep apnea in adults. AASM clinical guidance statement. J Clin Sleep Med 17(6):1287–1293.https://doi.org/10.5664/jcsm.9240

  6. Asadov A, Seepold R, Martínez Madrid N, Ortega JA (2022) Non-invasive cardiorespiration monitoring using force resistive sensor. In: Hardware and software supporting physiological measurement (HSPM-2022); Hochschule Reutlingen, Reutlingen, Germany, pp 20–22. https://doi.org/10.34645/opus-4001

  7. Fan D, Ren A, Zhao N, Haider D, Yang X, Tian J (2019) Small-scale perception in medical body area networks. IEEE J Transl Eng Health Med 7:1–11, Art no. 2700211. https://doi.org/10.1109/JTEHM.2019.2951670

  8. Boiko A, Martínez Madrid N, Seepold R (2023) Contactless technologies, sensors, and systems for cardiac and respiratory measurement during sleep: a systematic review. Sensors 23(11):5038. https://doi.org/10.3390/s23115038

    Article  Google Scholar 

  9. Malik AR, Boger J (2021) Zero-effort ambient heart rate monitoring using ballistocardiography detected through a seat cushion: prototype development and preliminary study. JMIR Rehabil Assist Technol 8:e25996. https://doi.org/10.2196/25996

    Article  Google Scholar 

  10. Xu W, Yu C, Dong B, Wang Y, Zhao W (2022) Thin piezoelectric sheet assisted PGC demodulation of fiber-optic integrated MZI and its application in under mattress vital signs monitoring. IEEE Sens J 22:2151–2159. https://doi.org/10.1109/JSEN.2021.3128601

    Article  Google Scholar 

  11. Gomez-Clapers J, Serra-Rocamora A, Casanella R, Pallas-Areny R (2014) Towards the standardization of ballistocardiography systems for J-peak timing measurement. Measurement 58:310–316. https://doi.org/10.1016/j.measurement.2014.09.003

    Article  Google Scholar 

  12. D’Mello Y et al (2019) Real-time cardiac beat detection and heart rate monitoring from combined seismocardiography and gyrocardiography. Sensors 19:3472. https://doi.org/10.3390/s19163472

    Article  Google Scholar 

  13. Boiko A, Scherz WD, Gaiduk M, Gentili A, Conti M, Orcioni S, Seepold R, Martínez Madrid N (2022) Sleep respiration rate detection using an accelerometer sensor with special holder setup. In: Proceedings of the 2022 E-Health and bioengineering conference (EHB), pp 1–4. https://doi.org/10.1109/EHB55594.2022.9991578

  14. Boiko A, Gaiduk M, Martínez Madrid N, Seepold R (2023) Evaluation of a prototype for early active patient mobilization. Procedia Computer Science (Accepted)

    Google Scholar 

  15. Conti M, Aironi C, Orcioni S, Seepold R, Gaiduk M, Martínez Madrid N (2020) Heart rate detection with accelerometric sensors under the mattress. In: 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC). pp 4063–4066. https://doi.org/10.1109/EMBC44109.2020.9175735

  16. Hersek S, Semiz B, Shandhi MMH, Orlandic L, Inan OT (2020) A globalized model for mapping wearable seismocardiogram signals to whole-body ballistocardiogram signals based on deep learning. IEEE J Biomed Health Inf 24(5):1296–1309. https://doi.org/10.1109/JBHI.2019.2931872

    Article  Google Scholar 

  17. Skoric J et al (2022) Respiratory modulation of sternal motion in the context of seismocardiography. IEEE Sens J 22(13):13055–13066. https://doi.org/10.1109/JSEN.2022.3173205

    Article  Google Scholar 

  18. Sadek I, Seet E, Biswas J, Abdulrazak B, Mokhtari M (2018) Nonintrusive vital signs monitoring for sleep Apnea patients: a preliminary study. IEEE Access 6:2506–2514. https://doi.org/10.1109/ACCESS.2017.2783939

    Article  Google Scholar 

  19. Ullal et al (2021) Non-invasive monitoring of vital signs for older adults using recliner chairs. Health Technol 11:169–184. https://doi.org/10.1007/s12553-020-00503-9

    Article  Google Scholar 

  20. Gaiduk M et al (2022) Estimation of sleep stages analyzing respiratory and movement signals. IEEE J Biomed Health Inf 26(2):505–514. https://doi.org/10.1109/JBHI.2021.3099295

    Article  Google Scholar 

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Acknowledgments

This research was partially funded by Carl Zeiss Foundation and the MORPHEUS-Project “Non-invasive system for measuring parameters relevant to sleep quality” (project number: P2019–03-003).

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Authors and Affiliations

  1. Ubiquitous Computing Lab, Department of Computer Science, HTWG Konstanz—University of Applied Sciences, 78462, Konstanz, Germany

    Andrei Boiko, Maksym Gaiduk & Ralf Seepold

  2. School of Informatics, Reutlingen University, 72762, Reutlingen, Germany

    Natividad Martínez Madrid

Authors
  1. Andrei Boiko
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  2. Maksym Gaiduk
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  3. Natividad Martínez Madrid
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  4. Ralf Seepold
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Corresponding author

Correspondence to Andrei Boiko .

Editor information

Editors and Affiliations

  1. Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy

    Francesco Bellotti

  2. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece

    Miltos D. Grammatikakis

  3. Lab-STICC, CNRS UMR 6285, École nationale supérieure de techniques avancées Bretagne, Brest, France

    Ali Mansour

  4. Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy

    Massimo Ruo Roch

  5. Department of Computer Science, HTWG Konstanz—University of Applied Sciences, Konstanz, Germany

    Ralf Seepold

  6. Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain

    Agusti Solanas

  7. Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy

    Riccardo Berta

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Boiko, A., Gaiduk, M., Madrid, N.M., Seepold, R. (2024). Evaluation of a Contactless Accelerometer Sensor System for Heart Rate Monitoring During Sleep. In: Bellotti, F., et al. Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2023. Lecture Notes in Electrical Engineering, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-031-48121-5_47

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  • DOI: https://doi.org/10.1007/978-3-031-48121-5_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48120-8

  • Online ISBN: 978-3-031-48121-5

  • eBook Packages: EngineeringEngineering (R0)

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