Maternal healthcare using IoT-based integrated medical device: Bangladesh perspective

https://doi.org/10.35912/ijamis.v3i2.3288
Published: Aug 7, 2025

Abstract:

Purpose: The main purpose of this study was to develop a low-cost integrated medical device. This device will help investigate the risk levels of pregnant patients and reduce the cost of medical diagnosis for poor countries such as Bangladesh, where maternal healthcare is a great concern.

Research Methodology: A device equipped with multiple sensors was developed to collect raw data from pregnant patients. This data is transmitted to the cloud, where open-source algorithms process and analyze it to identify patient risk levels.

Results: We developed the system, collected raw data from patients, and uploaded these data to our cloud system. The data were processed in the cloud, and the resultant data were presented in the form of graphs. From these graphs, the risk levels were determined.

Conclusion: The IoT-based integrated device showed approximately 93% accuracy compared with conventional methods. It is a cost-effective, scalable, and adaptable solution that is suitable for maternal healthcare in developing countries. Features such as plug-and-play sensors, real-time cloud processing, and machine learning-based diagnostics make it a promising innovation for reducing maternal and infant mortality rates.

Limitations: The device is designed solely for use in pregnant patients and requires authorization from health regulators. Some high-cost sensors were excluded to ensure affordability..

Contribution: The main contribution of this study is to minimize the costs involved in maternal healthcare in poor countries such as Bangladesh. This, in turn, controls the death of mothers and children by improving maternal healthcare facilities.

Keywords:
1. Cloud
2. Cyber-Physical Systems
3. Integrated Medical Devices
4. Open Source Code
5. Processing Algorithm
Authors:
1 . Mohammod Abul Kashem
Dhaka University of Engineering & Technology, Bangladesh
2 . Marzia Ahmed
Dhaka University of Engineering & Technology, Bangladesh
3 . Naderuzzaman Mohammad
Sonargaon University, Bangladesh
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How to Cite
Kashem, M. A. ., Ahmed, M., & Mohammad, N. (2025). Maternal healthcare using IoT-based integrated medical device: Bangladesh perspective. International Journal of Accounting and Management Information Systems, 3(2), 85–99. https://doi.org/10.35912/ijamis.v3i2.3288
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Issue & Section
Vol. 3 No. 2 (2025): August
Articles
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References

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    Munyao, M. M., Maina, E. M., Mambo, S. M., & Wanyoro, A. (2024). Real-time pre-eclampsia prediction model based on IoT and machine learning. Discover Internet of Things, 4(1), 10. doi:https://doi.org/10.1007/s43926-024-00063-8

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    Oh, A.-S. (2015). A Study on HL7 Standard Message for Healthcare System Based on ISO/IEEE 11073. International Journal of Smart Home, 9(6), 113-118.

    Palanisamy, V., & Thirunavukarasu, R. (2019). Implications of big data analytics in developing healthcare frameworks–A review. Journal of King Saud University-Computer and Information Sciences, 31(4), 415-425. doi:https://doi.org/10.1016/j.jksuci.2017.12.007

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    Pratama, D. B., & Armin, A. P. (2025). Pengembangan Sistem Informasi Aplikasi Mobile Pelayanan Elektronik Dispendukcapil Kota Malang. Jurnal Ilmu Siber dan Teknologi Digital, 3(1), 11-41. doi:10.35912/jisted.v3i1.5098

    Prosperi, M., Min, J. S., Bian, J., & Modave, F. (2018). Big data hurdles in precision medicine and precision public health. BMC medical informatics and decision making, 18(1), 139. doi:https://doi.org/10.1186/s12911-018-0719-2

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    Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.

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    Zhang, Y., Qiu, M., Tsai, C.-W., Hassan, M. M., & Alamri, A. (2015). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88-95.

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  2. Akila1, A., Parameswari, R., & Jayakumari, C. (2022). Big data in healthcare: Management, analysis, and future prospects. Handbook of Intelligent Healthcare Analytics: Knowledge Engineering with Big Data Analytics, 309-326. doi:https://doi.org/10.1002/9781119792550.ch14
  3. Alzahrani, A., Alshehri, M., AlGhamdi, R., & Sharma, S. K. (2023). Improved wireless medical cyber-physical system (IWMCPS) based on machine learning. Paper presented at the Healthcare.
  4. Amadea, E., Suryaputra, R., & Sondakh, O. (2022). The effect of product quality, service quality, environment quality, and product assortment on customer loyalty trough customer satisfaction of BCA mobile application. J. Econ. Financ. Manag. Stud, 5(03). doi:https://doi.org/10.47191/jefms/v5-i3-17
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  9. Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of big data, 6(1), 1-25. doi:https://doi.org/10.1186/s40537-019-0217-0
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  13. Fitriana, D., Rahmadi, A. A., & Armin, A. P. (2024). Rancang Bangun Sistem Informasi Manajemen Praktik Mandiri Dokter Gigi Berbasis Website. Jurnal Ilmu Siber dan Teknologi Digital, 3(1), 63-84. doi:10.35912/jisted.v3i1.5096
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  23. Nakajima, H., Shiga, T., & Hata, Y. (2012). Systems health care: Coevolutionary integration of smart devices and smart services. Paper presented at the 2012 Annual SRII Global Conference.
  24. Oh, A.-S. (2015). A Study on HL7 Standard Message for Healthcare System Based on ISO/IEEE 11073. International Journal of Smart Home, 9(6), 113-118.
  25. Palanisamy, V., & Thirunavukarasu, R. (2019). Implications of big data analytics in developing healthcare frameworks–A review. Journal of King Saud University-Computer and Information Sciences, 31(4), 415-425. doi:https://doi.org/10.1016/j.jksuci.2017.12.007
  26. Perejón, D., Bardalet, A., Gascó, I., Siscart, J., Serna, M. C., & Orós, M. (2024). Hypertension subtypes and adverse maternal and perinatal outcomes-a retrospective population-based cohort study. BMC pregnancy and childbirth, 24(1), 568. doi:https://doi.org/10.1186/s12884-024-06754-y
  27. Phuong, L. T. T., Hieu, N. T., Wang, J., Lee, S., & Lee, Y.-K. (2011). Energy efficiency based on quality of data for cyber physical systems. Paper presented at the 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing.
  28. Pratama, D. B., & Armin, A. P. (2025). Pengembangan Sistem Informasi Aplikasi Mobile Pelayanan Elektronik Dispendukcapil Kota Malang. Jurnal Ilmu Siber dan Teknologi Digital, 3(1), 11-41. doi:10.35912/jisted.v3i1.5098
  29. Prosperi, M., Min, J. S., Bian, J., & Modave, F. (2018). Big data hurdles in precision medicine and precision public health. BMC medical informatics and decision making, 18(1), 139. doi:https://doi.org/10.1186/s12911-018-0719-2
  30. Putri, N. S., Budiarti, E., Huboyo, H. S., & Haryanti, N. (2024). Perencanaan Strategi Reduksi Emisi Gas Rumah Kaca pada Sektor Energi. Jurnal Teknologi Riset Terapan, 2(1), 37-53. doi:10.35912/jatra.v2i1.4602
  31. Riesna, D. M. R., Pujianto, D. E., Efendi, A. J. I., Nugroho, B. A., & Saputra, D. I. S. (2023). Identifikasi Platform dan Faktor Sukses dalam Manajemen Proyek Teknologi Informasi. Jurnal Teknologi Riset Terapan, 1(1), 1-9. doi:10.35912/jatra.v1i1.1458
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  34. Shaikh, T. A., Rasool, T., & Verma, P. (2023). Machine intelligence and medical cyber-physical system architectures for smart healthcare: Taxonomy, challenges, opportunities, and possible solutions. Artificial Intelligence in Medicine, 146, 102692. doi:https://doi.org/10.1016/j.artmed.2023.102692
  35. Shakor, M. Y., & Khaleel, M. I. (2024). Recent advances in big medical image data analysis through deep learning and cloud computing. Electronics, 13(24), 4860. doi:https://doi.org/10.3390/electronics13244860
  36. Soegijoko, S. (2013). A brief review on existing cyber-physical systems for healthcare applications and their prospective national developments. Paper presented at the 2013 3rd International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering (ICICI-BME).
  37. Sulisworo, D., Erviana, V. Y., Rosyady, P. A., Astuti, D. A., Rasyid, E., Bakhtiar, R., . . . Duma, K. (2025). Wearable IoT for Maternal Healthcare: A Literature Review on Implementation, Challenges, and Future Prospects. Bincang Sains dan Teknologi, 4(01), 51-60. doi:https://doi.org/10.56741/bst.v4i01.817
  38. Torab-Miandoab, A., Samad-Soltani, T., Jodati, A., & Rezaei-Hachesu, P. (2023). Interoperability of heterogeneous health information systems: a systematic literature review. BMC medical informatics and decision making, 23(1), 18. doi:https://doi.org/10.1186/s12911-023-02115-5
  39. Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.
  40. Yang, X., Liu, L., & Wang, Y. (2024). A Decision Tree-Driven IoT systems for improved pre-natal diagnostic accuracy. BMC medical informatics and decision making, 24(1), 375. doi:https://doi.org/10.1186/s12911-024-02759-x
  41. Zhang, Y., Qiu, M., Tsai, C.-W., Hassan, M. M., & Alamri, A. (2015). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88-95.