Low-cost Physiological Parameter Development using Internet of Things Based for Monitoring Health Elderly
DOI:
https://doi.org/10.3889/oamjms.2022.8818Keywords:
Low Cost, Physiological Parameter, IoT, ElderlyAbstract
BACKGROUND: In today’s digital era, the development of technology and information is so fast not only in the world of medicine and medical equipment but also in the model of health services so that many e-services are found, such as Alodokter and Halodoc. As well Internet of Things (IoT)-based technology IoT makes the method that can be used for remote services easy to reach and low cost, this is very significant in helping home care services in the elderly.
AIM: The goal of this research is to develop the design of telehealthcare based on IoT, especially the vital signs of monitoring for the early detection of diseases in the elderly through health-care services.
METHODS: This type of research is experimental with the design of equipment design using IoT based with parameters of a biomedical temperature sensor, heart rate, and SpO2 sensor for monitoring health elderly integrated into smartphone applications through programming Arduino ESP 32 microcontroller as a transmitter.
RESULTS: The results of this study consist of two stages, including first determining the accuracy value of biomedical sensor data results by measuring the error factor, namely, for beats per minute sensor, data have a deviation error of 1.6 and SpO2 deviation error of 0.25 and temperature deviation error of 0.16 with a confidence level of 0.05% and second comparing parameter values to standard values using t-test tests with p > 0.05 results means that there is no significant difference between parameter values and standard values.
CONCLUSION: The results of this study can be concluded that the physiological parameters, such as spo2, bpm and body temperature can be used for health monitoring in the elderly, and it is hoped that the results of this research design can be used for early detection of the elderly for routine health checks using a smartphone application.Downloads
Metrics
Plum Analytics Artifact Widget Block
References
Yu L, Chan WM, Zhao Y, Tsui KL. Personalized health monitoring system of elderly wellness at the community level in Hong Kong. IEEE Access. 2018;6:35558-67. https://doi.org/10.1109/access.2018.2848936 DOI: https://doi.org/10.1109/ACCESS.2018.2848936
Imran, Iqbal N, Ahmad S, Kim DH. Health monitoring system for elderly patients using intelligent task mapping mechanism in the closed-loop healthcare environment. Symmetry (Basel). 2021;13(2):357. https://doi.org/10.3390/sym13020357 DOI: https://doi.org/10.3390/sym13020357
Leichsenring K. Developing integrated health and social care services for older persons in Europe. Int. J Integr Care. 2004;4:e10. DOI: https://doi.org/10.5334/ijic.107
Leikola S. Development and Application of Comprehensive Medication Review Procedure to Community-dwelling Elderly. Doctoral dissertation; 2012.
Goodwin DM, Higginson IJ, Myers K, Douglas HR, Normand CE. Effectiveness of palliative daycare in improving pain, symptom control, and quality of life. J Pain Symptom Manage. 2003;25(3):202-12. https://doi.org/10.1016/s0885-3924(02)00688-7 PMid:12614955 DOI: https://doi.org/10.1016/S0885-3924(02)00688-7
Verulava T, Adeishvili I, Maglakelidze T. Home care services for elderly people in Georgia. Home Health Care Manag Pract. 2016;28(3):170-7. https://doi.org/10.1177/1084822315627785 DOI: https://doi.org/10.1177/1084822315627785
Pires P, Mendes L, Mendes J, Rodrigues R, Pereira A. Integrated e-healthcare system for elderly support. Cognit Comput. 2016;8(2):368-84. https://doi.org/10.1007/s12559-015-9367-3 DOI: https://doi.org/10.1007/s12559-015-9367-3
Kumar N. IoT Architecture and System Design for Healthcare Systems. In: Proceeding 2017 International Conference on Smart Technologies For Smart Nation; 2018. p. 1118-23. https://doi.org/10.1109/smarttechcon.2017.8358543 DOI: https://doi.org/10.1109/SmartTechCon.2017.8358543
Dimitrov DV. Medical internet of things and big data in healthcare. Healthc Inform Res. 2016;22(3):156-63. https://doi.org/10.4258/hir.2016.22.3.156 PMid:27525156 DOI: https://doi.org/10.4258/hir.2016.22.3.156
Sharma D, Tripathi RC. Performance of Internet of Things (IoT) Based Healthcare Secure Services and Its Importance: Issue and Challenges. In: Proceedings of the International Conference on Innovative Computing Communications (ICICC) 2020; 2015. p. 1-4. https://doi.org/10.2139/ssrn.3565782 DOI: https://doi.org/10.2139/ssrn.3565782
Dziak D, Jachimczyk B, Kulesza WJ. IoT-based information system for healthcare application: design methodology approach. Appl Sci. 2017;7(6):596. https://doi.org/10.3390/app7060596 DOI: https://doi.org/10.3390/app7060596
Nilsen W, Kumar S, Shar A, Varoquiers C, Wiley T, Riley WT. Advancing the science of mHealth. J Health Commun. 2012;17(Suppl 1):5-10. https://doi.org/10.1080/10810730.2012.677394 PMid:22548593 DOI: https://doi.org/10.1080/10810730.2012.677394
Depolli M, Avbelj V, Trobec R, Kališnik JM, Korošec T, Susič AP, et al. PCARD platform for mhealth monitoring. Inform. 2016;40(1):117-23.
Joshi S, Khan A. Overview: Study of a various factors needed for biomedical embedded system. Int J Eng Appl Sci Technol. 2019;3(12):59-62. https://doi.org/10.33564/ijeast.2019.v03i12.009 DOI: https://doi.org/10.33564/IJEAST.2019.v03i12.009
Asra Noorain F, Raju J, Varsha V, Nanditha HG. An IoT based approach to minimize and monitor air pollution using ESP32 and blynk platform. J Xi’an Univ Archit Technol. 2020;12(6):558-66.
Thaung SM, Tun HM, Win KK, Than MM, Phyo AS. Exploratory data analysis based on remote health care monitoring system by using IoT. Communications. 2020;8(1):1-8. https://doi.org/10.11648/j.com.20200801.11 DOI: https://doi.org/10.11648/j.com.20200801.11
Bohara B, Maharjan S, Shrestha BR. IoT based smart home using blynk framework. Zerona Sch. 2020;1(1):26-30. https://doi.org/10.48550/arXiv.2007.13714
Huang CH, Guo JW. Design of reflectance pulse oximeter and bpm using the max30100 sensor in early detection of hypoxemia in patients with cardiovascular disorders. Int J Adv Health Sci Technol. 2021;1(1):1-6. https://doi.org/10.35882/ijahst.v1i1.1 DOI: https://doi.org/10.35882/ijahst.v1i1.1
Durani H, Sheth M, Vaghasia M, Kotech S. Smart Automated Home Application Using IoT with Blynk App. In: Proceedings International Conference on Computational Intelligence and Communication Technologies ICICCT 2018; 2018. p. 393-7. https://doi.org/10.1109/ICICCT.2018.8473224 DOI: https://doi.org/10.1109/ICICCT.2018.8473224
Hamzah T, Setioningsih ED, Sumber S. Microcontrollers performance in portable electronic stethoscope design with a disease symptoms detection feature. Int J Adv Health Sci Technol. 2021;1(2):61-7. https://doi.org/10.35882/ijahst.v1i2.5 DOI: https://doi.org/10.35882/ijahst.v1i2.5
Babiuch M, Foltýnek P, Smutný P. Using the ESP32 Microcontroller for Data Processing. In: International Carpathian Control Conference (ICCC). Piscataway: Institute of Electrical and Electronics Engineers; 2019. p. 1-6. https://doi.org/10.1109/carpathiancc.2019.8765944 DOI: https://doi.org/10.1109/CarpathianCC.2019.8765944
Ranjana S, Hegde R, Divya CD. Real-Time Patient Monitoring System Using BLYNK. In: IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). Piscataway: Institute of Electrical and Electronics Engineers; 2021. p. 327-31. https://doi.org/10.1109/discover52564.2021.9663681 DOI: https://doi.org/10.1109/DISCOVER52564.2021.9663681
Mee RW, Chua TC. Regression toward the mean and the paired sample t-test. Am Stat. 1991;45(1):39-42. https://doi.org/10.2307/2685237 DOI: https://doi.org/10.1080/00031305.1991.10475763
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2022 Bedjo Utomo, Triwiyanto Triwiyanto, Sari Luthfiyah, Wuri Ratna Hidayani, Lukman Handoko (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
http://creativecommons.org/licenses/by-nc/4.0
