skip to main content
10.1145/3407982.3408009acmotherconferencesArticle/Chapter ViewAbstractPublication PagescompsystechConference Proceedingsconference-collections
research-article

Body Sensors System for Physiological Data Long-term Monitoring

Published:25 August 2020Publication History

ABSTRACT

The modern sensors can be used to create non-invasive, interconnected, adaptive, intelligent, dynamic systems that capture and analyze the physiological data and generate signals when life-saving action is required. The aim of this paper is to present and evaluate an information system for long term monitoring of physiological data registered through modern body sensors. The presented hardware and software system is based on the use of four photoplethysmographic sensors and one electrocardiographic sensor placed at different places on the human body. The photoplethysmographic sensors are placed two on the left side of the body and two on the right side, which allows studying the influence of cardiac activity on both halves of the body. The advantages of physiological monitoring realized with a system of five independent, simultaneously recorded signals from different parts of the human body are shown. The recorded signals are preprocessing in a modern technological microcontroller environment. The software part of the information system is implemented with the help of the cloud computing, through which technologies achieve efficient operation and interaction between the used sensors and systems for health prevention and timely signaling in situations of risk. The use of an integrated sensor-based system enables the refinement of physiological information submitted to the software system for data analysis and generation of effective action solutions.

References

  1. E. Gospodinova, M. Gospodinov, N. Dey. I. Domuschiev, A. S. Ashou, S. V. Balas, T. Olariu (2018). Specialized Software System for Heart Rate Variability Analysis: An Implementation of Nonlinear Graphical Methods. In: Balas V., Jain L., Balas M. (eds). Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-319-62521-8_31Google ScholarGoogle ScholarCross RefCross Ref
  2. A. Choudhury, S. Samanta, N. Dey, A.S. Ashour, D. Bălas-Timar, M. Gospodinov, E. Gospodinova (2015). Microscopic Image Segmentation Using Quantum Inspired Evolutionary Algorithm. Journal of Advanced Microscopy Research, Volume 10, Number 3, pp. 164--173. https://doi.org/10.1166/jamr.2015.1257Google ScholarGoogle ScholarCross RefCross Ref
  3. Christov I., I. Jekova, V. Krasteva, I. Dotsinsky, T. Stoyanov (2009). Rhythm Analysis by Heartbeat Classification in the Electrocardiogram, International Journal Bioautomation, 13(2), 84--96.Google ScholarGoogle Scholar
  4. Jekova I., V. Tsibulko, I. Iliev (2014). ECG Database Applicable for Development and Testing of Pace Detection Algorithms, International Journal Bioautomation, 18(4), 377--388.Google ScholarGoogle Scholar
  5. Krasteva V., Jekova I., and Ramun Schmid. (2019) Krasteva, Vessela et al. "Simulating Arbitrary Electrode Reversals in Standard 12-lead ECG." Sensors (Basel, Switzerland) vol. 19 (13): 2920., doi:10.3390/s19132920.Google ScholarGoogle ScholarCross RefCross Ref
  6. H. Ouyang, J. Tian, G. Sun, Y. Zou, Z. Liu, H. Li, L. Zhao, B. Shi, Y. Fan, Y. Fan, and Z. L. Wang, "Self-Powered Pulse Sensor for Antidiastole of Cardiovascular Disease," Adv. Mater., 29(40), 1--10, 2017. DOI: 10.1002/adma.201703456Google ScholarGoogle Scholar
  7. S. Kale, S. Mane, and P. Patil, "IOT based wearable biomedical monitoring system," Proceedings of the International Conference on Trends in Electronics and Informatics, 2017, pp. 971--976. DOI:10.1109/icoei.2017.8300852Google ScholarGoogle Scholar
  8. M. Taştan. IoT Based Wearable Smart Health Monitoring System. Celal Bayar University Journal of Science Volume 14, Issue 3, 2018, p 343--350. DOI: 10.18466/cbayarfbe.451076Google ScholarGoogle Scholar
  9. Francesco Rundo, Sabrina Conoci, Alessandro Ortis, and Sebastiano Battiato, An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment, Sensors 2018, 18(2), 405; https://doi.org/10.3390/s18020405.Google ScholarGoogle Scholar
  10. M. Ghamari; C. Soltanpur; S. Cabrera; R Romero; R Martinek; H. Nazera. Design and prototyping of a wristband-type wireless photoplethysmographic device for heart rate variability signal analysis. Published in: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Date of Conference: 16-20 Aug. 2016. DOI: 10.1109/EMBC.2016.7591842Google ScholarGoogle Scholar
  11. M. Bentham, G. Stansby, and J. Allen. Innovative Multi-Site Photoplethysmography Analysis for Quantifying Pulse Amplitude and Timing Variability Characteristics in Peripheral Arterial Disease. Diseases, 2018, 6(3); 81. DOI: 10.3390/diseases6030081Google ScholarGoogle Scholar
  12. Fujita D., Suzuki A., Ryu K., PPG-based Systolic Blood Pressure Estimation Method using PLS and Level-crossing Feature. Applied Sciences. 2019, 9, 304, DOI: 10.3390/app9020304.Google ScholarGoogle Scholar
  13. G. Acampora, D. Cook, P. Rashidi, A. Vasilakos. A Survey on Ambient Intelligence in Health Care. Proceeding of the IEEE, December 2013, DOI: 10.1109/JPROC.2013.2262913Google ScholarGoogle Scholar
  14. Fan, Q.; Li, K., 2018. Non-contact remote estimation of cardiovascular parameters. Biomed. Signal Process. Control, 40, 192--203. DOI: 10.1016/j.bspc.2017.09.022Google ScholarGoogle ScholarCross RefCross Ref
  15. J. L. Moraes, M. X. Rocha, G. G. Vasconcelos, J. E. V. Filho, V. de Albuquerque, and A. R. Alexandria, Advances in Photoplethysmography Signal Analysis for Biomedical Applications, Sensors, s 2018, 18, 1894, DOI:10.3390/s18061894Google ScholarGoogle Scholar
  16. Sun, Y.; Thakor, N. Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging. IEEE Trans. Biomed. Eng. 2016, 63, 463--477. DOI: 10.1109/TBME.2015.2476337.Google ScholarGoogle Scholar
  17. Georgieva-Tsaneva G. Wavelet Based Interval Varying Algorithm for Optimal Non-Stationary Signal Denoising. Proceedings of the 20th International Conference on Computer Systems and Technologies, June 2019, Pages 200--206. DOI: https://doi.org/10.1145/3345252.3345268.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Gospodinov M., Cheshmedziev K., Three-Sensor Portable Information System for Physiological Data Registration. Proceedings of the 20th International Conference on Computer Systems and Technologies, June 2019 Pages 36--41, DOI: https://doi.org/10.1145/3345252.3345281Google ScholarGoogle Scholar
  1. Body Sensors System for Physiological Data Long-term Monitoring

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      CompSysTech '20: Proceedings of the 21st International Conference on Computer Systems and Technologies
      June 2020
      343 pages
      ISBN:9781450377683
      DOI:10.1145/3407982

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 25 August 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Acceptance Rates

      CompSysTech '20 Paper Acceptance Rate46of72submissions,64%Overall Acceptance Rate241of492submissions,49%
    • Article Metrics

      • Downloads (Last 12 months)6
      • Downloads (Last 6 weeks)1

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader