skip to main content
10.1145/3278576.3278596acmconferencesArticle/Chapter ViewAbstractPublication PageschConference Proceedingsconference-collections
research-article

IoT-based healthcare system for real-time maternal stress monitoring

Published:22 January 2020Publication History

ABSTRACT

There is a major concern about pregnancy-associated stress and anxiety, which are key risk factors for various pregnancy complications involving the health of mother and fetus [13, 14, 24, 32]. Maternal adaptations to decrease the stress level are important to enable a successful pregnancy although various maternal difficulties and environmental stressors can disrupt these adaptations. Several studies have tackled this subject, managing stress level during pregnancy with different medications and techniques [12, 22]. However, to support the conventional clinical methods, a personalized and automated healthcare system is highly required, providing stress monitoring for not only in-hospital environment but also everyday settings. Fortunately, recent advancements in Internet of Things (IoT) technologies have enabled the deployment of remote health monitoring systems in real-time applications, of which patient's health-associated parameters are continuously collected and analyzed to deliver health services.

References

  1. Saeed Aghabozorgi, Ali Seyed Shirkhorshidi, and Teh Ying Wah. Time-series clustering - a decade review. Information Systems, 53:16--38, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. A. Al-Fuqaha et al. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Commun. Surveys & Tuts, 17(4):2347--76, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  3. A. Alberdi et al. Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. Journal of Biomedical Informatics, 59:49--75, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. L. Atzori et al. The internet of things: A survey. Computer Networks, 54(15):2787--805, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Jorn Bakker, Leszek Holenderski, Rafal Kocielnik, Mykola Pechenizkiy, and Natalia Sidorova. Stess@work: From measuring stress to its understanding, prediction and handling with personalized coaching. Proceedings of the 2nd ACM SIGHIT symposium on International health informatics - IHI 12, Jan 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. George Boateng and David Kotz. Stressaware: An app for real-time stress monitoring on the amulet wearable platform. 2016 IEEE MIT Undergraduate Research Technology Conference (URTC), 2016.Google ScholarGoogle ScholarCross RefCross Ref
  7. R. E. Carpenter, S. J. Emery, O. Uzun, D. Rassi, and M. J. Lewis. Influence of antenatal physical exercise on heart rate variability and qt variability. The Journal of Maternal-Fetal & Neonatal Medicine, 30(1):79--84, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  8. Ying Chen, Wenxi Chen, Kei-ichiro Kitamura, and Tetsu Nemoto. Long-term measurement of maternal pulse rate dynamics using a home-based sleep monitoring system. Journal of Sensors, 2016:1--11, 2016.Google ScholarGoogle Scholar
  9. Anna Choromanska and Claire Monteleoni. Online clustering with experts. International Conference on Artificial Intelligence and Statistics, 2012.Google ScholarGoogle Scholar
  10. J.F. Clapp. Maternal heart rate in pregnancy. American Journal of Obstetrics and Gynecology, 152(6):659--60, 1985.Google ScholarGoogle ScholarCross RefCross Ref
  11. James F. Clapp III. Maternal heart rate in pregnancy. American Journal of Obstetrics & Gynecology, Jul 1985.Google ScholarGoogle Scholar
  12. J.A.and others DiPietro. Measuring the ups and downs of pregnancy stress. J Psychosom Obstet Gynaecol., 25(3--4):189--201, 2004.Google ScholarGoogle Scholar
  13. C. Dunkel-Schetter. Psychological science on pregnancy: stress processes, biopsy-chosocial models, and emerging research issues. Annu Rev Psychol., 62:531--58, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  14. C. Dunkel-Schetter and L. Tanner. Anxiety, depression and stress in pregnancy: implications for mothers, children, research, and practice. Curr Opin Psychiatry., 25(2):141--8, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  15. J. Gama et al. A survey on concept drift adaptation. ACM Computing Surveys, 46(4), 2014.Google ScholarGoogle Scholar
  16. Garmin. Vivosmart 3 - heart rate variability and stress level, 2018. https://www8.garmin.com/manuals/webhelp/vivosmart3/EN-US/GUID-9282196F-D969-404D-B678-F48A13D8D0CB.html.Google ScholarGoogle Scholar
  17. Research Gate. Which are the methods to validate an unsupervised machine learning algorithm?, 2018. https://www.researchgate.net/post/Which_are_the_methods_to_validate_an_unsupervised_machine_learning_algorithm.Google ScholarGoogle Scholar
  18. M. Gjoreski et al. Monitoring stress with a wrist device using context. Journal of Biomedical Informatics, 73:159--70, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. J. Gubbi et al. Internet of things (iot): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7):1645--60, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. E. Hall et al. The heart during pregnancy. Rev Esp Cardiol., 64(11):1045--50, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  21. Michael E. Hall, Eric M. George, and Joey P. Granger. The heart during pregnancy. Revista Española de Cardiología, 64(11):1048, 2011.Google ScholarGoogle Scholar
  22. J.G. Hamilton and Lobel M. Types, patterns, and predictors of coping with stress during pregnancy: Examination of the revised prenatal coping inventory in a diverse sample. J Psychosom Obstet Gynaecol., 29(2):97--104, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  23. John Hart. Association between heart rate variability and manual pulse rate. The Canadian Chiropractic Association.Google ScholarGoogle Scholar
  24. K.M. Hillerer et al. Exposure to chronic pregnancy stress reverses peripartum-associated adaptations: implications for postpartum anxiety and mood disorders. Endocrinology, 152(10):3930--40, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  25. Syed Zaki Hassan Kazmi, Henggui Zhang, Wajid Aziz, Oliver Monfredi, Syed Ali Abbas, Saeed Arif Shah, Syeda Sobia Hassan Kazmi, and Wasi Haider Butt. Inverse correlation between heart rate variability and heart rate demonstrated by linear and nonlinear analysis. Plos One, 11(6), 2016.Google ScholarGoogle Scholar
  26. Hye-Geum Kim, Eun-Jin Cheon, Dai-Seg Bai, Young Hwan Lee, and Bon-Hoon Koo. Stress and heart rate variability: A meta-analysis and review of the literature. Psychiatry Investigation, 15(3):235--245, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  27. Angie King. Online k-Means Clustering of Non-stationary Data. 2012.Google ScholarGoogle Scholar
  28. S. Mayya et al. Continuous monitoring of stress on smartphone using heart rate variability. In 15th International Conference on Bioinformatics and Bioengineering (BIBE), 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. M.G. Moertl et al. Changes in haemodynamic and autonomous nervous system parameters measured non-invasively throughout normal pregnancy. Eur J Obstet Gynecol Reprod Biol., 144:S179--83, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  30. Jamalali Moiz and Pooja Bhati. Correlation of heart rate with heart rate variability in sedentary young indian women. Saudi Journal of Sports Medicine, 17(2):105, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  31. Mário W. L. Moreira, Joel J. P. C. Rodrigues, Antonio M. B. Oliveira, and Kashif Saleem. Smart mobile system for pregnancy care using body sensors. International Conference on Selected Topics in Mobile & Wireless Networking (MoWNeT), 2016.Google ScholarGoogle ScholarCross RefCross Ref
  32. M. G. Motlagh et al. Severe psychosocial stress and heavy cigarette smoking during pregnancy: an examination of the pre- and perinatal risk factors associated with adhd and tourette syndrome. European Child & Adolescent Psychiatry, 19(10):755--64, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  33. Joo Eon Park, Ji Yeon Lee, Suk-Hoon Kang, Jin Hee Choi, Tae Yong Kim, Hyung Seok So, and In-Young Yoon. Heart rate variability of chronic post-traumatic stress disorder in the korean veterans. Psychiatry Research, 255:72--77, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  34. K. Peternel et al. A presence-based context-aware chronic stress recognition system. Sensors (Basel), 12(11):15888--906, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  35. A. M. Rahmani et al. Fog Computing in the Internet of Things Intelligence at the Edge. Springer, 2017.Google ScholarGoogle Scholar
  36. A. M. Rahmani et al. Exploiting smart e-health gateways at the edge of healthcare internet-of-things: A fog computing approach. FGCS, 78(2):641--58, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. M. SaeidMahdavinejad et al. Machine learning for internet of things data analysis: A survey. Digital Communications and Networks, 2017.Google ScholarGoogle Scholar
  38. Toru Seo, Takahiko Kusakabe, Hiroto Gotoh, and Yasuo Asakura. Interactive online machine learning approach for activity-travel survey. Transportation Research Part B: Methodological, 2017.Google ScholarGoogle Scholar
  39. Nidhi Singh and Divakar Singh. Performance evaluation of k-means and hierarchical clustering in terms of accuracy and. International Journal of Computer Science and Information Technologies, Vol 3, 2012.Google ScholarGoogle Scholar
  40. Joachim Taelman, S. Vandeput, A. Spaepen, and S. Van Huffel. Influence of mental stress on heart rate and heart rate variability. IFMBE Proceedings 4th European Conference of the International Federation for Medical and Biological Engineering, page 1366--1369, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  41. T. G. M. Vrijkotte, L. J. P. Van Doornen, and E. J. C. De Geus. Effects of work stress on ambulatory blood pressure, heart rate, and heart rate variability. Hypertension, 35(4):880, Jan 2000.Google ScholarGoogle ScholarCross RefCross Ref
  42. S. Yoon et al. A flexible and wearable human stress monitoring patch. Sci. Rep., 6, 2016.Google ScholarGoogle Scholar

Index Terms

  1. IoT-based healthcare system for real-time maternal stress monitoring
        Index terms have been assigned to the content through auto-classification.

        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 Conferences
          CHASE '18: Proceedings of the 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies
          September 2018
          139 pages
          ISBN:9781450359580
          DOI:10.1145/3278576

          Copyright © 2018 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: 22 January 2020

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader