vf Empowering Health and Well-being: IoT-Driven Vital Signs Monitoring in Educational Institutions and Elderly Homes Using Machine Learning
FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-12bdf07

Research Article |

Empowering Health and Well-being: IoT-Driven Vital Signs Monitoring in Educational Institutions and Elderly Homes Using Machine Learning

Author(s): Nana Yaw Duodu, Warish D. Patel*, Hakan Koyuncu, Felix Nartey and Wisdom Torgby

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Special Issue on BDF

Publisher : FOREX Publication

Published : 28 March 2024

e-ISSN : 2347-470X

Page(s) : 40-47

DOI: https://doi.org/10.37391/ijeer.12bdf07

Abstract

IoT-based EHRs use machine learning technology to automate real-time patient-centered records more securely for authorized users. (1) Background: In this era of pandemics, predictive healthcare systems are necessary for private and public healthcare delivery to predict early cancer, COVID-19, hypertension, and fever in Educational Institutions and Elderly Homes. IoT-Based EHRs bring healthcare delivery to the doorsteps of educational home facilities users, thereby reducing the time required to access healthcare and minimizing direct physical interaction between individuals seeking healthcare and their providers. (2) Method: This research work proposed a real-time intelligent IoT-based EHR system that generates vital signs of students within the educational environment using contactless sensors (Raspberry Pi Noir Camera, rPPG camera) and contacted wearable sensors composed of enzymatic sensor, immunogens, and nanosensors to detect cancer (Leukaemia). AFTER CAPTURING THE PHYSIOLOGICAL DATA, THE in-build EWS plots system determines the condition and further triggers the criticality (abnormality) in health status. (3) Discussion: For effective health status prediction by the proposed plan, the vital sign dataset was used to train a model for the proposed method. Among the best-performing models, the random forest algorithm proved a better model, with an accuracy of 99.66% and an error rate of 0.34%. (4) Conclusion: The Home HMS seeks to improve health prediction in institutional homes for users' overall well-being.

Keywords: Internet of things, EHRs, IoT-based, EHR, Machine Learning, Educational Institutions and Elderly Homes, Health Monitoring.




Nana Yaw Duodu, Department of Computer Science and Engineering, Parul Institute of Engineering and Technology, Parul University, India; Email: 200300402013@paruluniversity.ac.in

Warish D. Patel*, Department of Computer Science and Engineering, Parul Institute of Engineering and Technology, Parul University, India; Email: warishkumar.patel@paruluniversity.ac.in

Hakan Koyuncu , Department of Computer Engineering, Altinbas University, Istanbul, Turkey; Email: hakan.koyuncu@altinbas.edu.tr

Felix Nartey, Department of Computer Science, Accra Technical University, Accra-Ghana; Email: fnartey@atu.edu.gh

Wisdom Torgby, Department of Computer Science, Accra Technical University, Accra-Ghana; Email: wtorgby@atu.edu.gh

    [1] Keshta I, Odeh A. Security and privacy of electronic health records: Concerns and challenges. Egypt Informatics J [Internet]. 2021;22(2):177–83. Available from: https://doi.org/10.1016/j.eij.2020.07.003.
    [2] Almutairi A, McCrindle R. Female student nurses attitudes towards electronic medical records in Riyadh City. Int Conf Electr Electron Optim Tech ICEEOT 2016. 2016;2366–70.
    [3] Atasoy H, Greenwood BN, McCullough JS. The Digitization of Patient Care: A Review of the Effects of Electronic Health Records on Health Care Quality and Utilization. Annu Rev Public Health. 2019;40:487–500.
    [4] Islam MS, Humaira F, Narin Nur DF. Healthcare Applications in IoT. Glob J Med Res. 2020;20(2):11–2.
    [5] Ghasempour A. Internet of things in smart grid: Architecture, applications, services, key technologies, and challenges. Inventions. 2019;4(1).
    [6] Ford W. Machine Learning for Beginners. A Compr Guide To Algorithms Mach Learn Data Sci. 2017;53(9):1689–99.
    [7] Aldahiri A, Alrashed B, Hussain W. Trends in Using IoT with Machine Learning in Health Prediction System. Forecasting. 2021;3(1):181–206.
    [8] Jayatilake SMDAC, Ganegoda GU. Involvement of Machine Learning Tools in Healthcare Decision Making. J Healthc Eng. 2021;2021.
    [9] Patel W, Patel C, Ramani B, Bhaskar S, Patel M. VitaFALL: NXTGeUH System for Well-Being Monitoring with Fall Recognition and Real-Time Vital Sign Monitoring. Int J Recent Technol Eng. 2020;8(5):5139–45.
    [10] Goyal D, Kumar A, Piuri V, Ganzha M. Lecture Notes in Networks and Systems 166 of the Second International Conference on Information Management and Machine Intelligence. 2020.
    [11] Gurjit Kaur, Tomar P, Tanque M. Artificial Intelligence to Solve Pervasive Internet of Things Issues. Artificial Intelligence to Solve Pervasive Internet of Things Issues. 2021.
    [12] Said AM, Yahyaoui A, Abdellatif T. Efficient anomaly detection for smart hospital IoT systems. Sensors (Switzerland). 2021;21(4):1–24.
    [13] Patel WD. Smart Health : Natural Language Processing based Question and Answering Retrieval System in Healthcare. 2019;6(5):1–7.
    [14] Harman R. How Is IoT Establishing The Modern Classroom? [Internet]. eLearning Industry. 2019. Available from: https://elearningindustry.com/iot-and-the-modern-classroom-establishing
    [15] Patel D. Warish, Chirag P, Patel Monal. Advanced Multi-Threshold Based Reliable Fall Detection System, Recent Advances in Computer Science and Communications. Recent Adv Comput Sci Commun. 2022;15(1):2666.
    [16] Tran DN, Nguyen TN, Khanh PCP, Trana DT. An IoT-based Design Using Accelerometers in Animal Behavior Recognition Systems. IEEE Sens J. 2021;XX(XX):1–14.
    [17] Nguyen DC, Ding M, Pathirana PN, Seneviratne A, Li J, Niyato D, et al. 6G Internet of Things: A Comprehensive Survey. IEEE Internet Things J. 2022;9(1):359–83.
    [18] Nguyen DiC, Cheng P, DIng M, Lopez-Perez D, Pathirana PN, Li J, et al. Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective. IEEE Commun Surv Tutorials. 2021;23(1):553–95.
    [19] Chakraborty C, Banerjee A, Garg L, Rodrigues JJPC. Internet of Medical Things for Smart Healthcare [Internet]. Chakraborty C, Banerjee A, Garg L, Rodrigues JJPC, editors. SSRN Electronic Journal. Singapore: Springer Nature Singapore Pte Ltd.; 2020. Available from: https://doi.org/10.1007/978-981-15-8097-0_1
    [20] Vaibhavee Jani, Patel W, Shah S. A Theoretical Evaluation of Machine Learning to Predict Enzyme Family. In: International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, 2022, [Internet]. Tuticorin, India: IEEE; 2022. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9752250&isnumber=97 51722.
    [21] Taeho Jo. Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning [Internet]. 2021. 391 p. Available from: https://link.springer.com/book/10.1007%2F978-3-030-65900-4.
    [22] Tunc MA, Gures E, Shayea I. A Survey on IoT Smart Healthcare: Emerging Technologies, Applications, Challenges, and Future Trends. 2021;1–15. Available from: http://arxiv.org/abs/2109.02042
    [23] Anita Gehlot, Rajesh Singh, Praveen Kumar Malik, Lovi Raj Gupta BS. Internet of Things with 8051 and ESP8266. CRC Press; 2021.
    [24] Desai F, Chowdhury D, Kaur R, Peeters M, Arya RC, Wander GS, et al. HealthCloud: A system for monitoring the health status of heart patients using machine learning and cloud computing. Internet of Things (Netherlands) [Internet]. 2022;17(December 2021):100485. Available from: https://doi.org/10.1016/j.iot.2021.100485.
    [25] Pinge A, Bandyopadhyay S, Ghosh S, Sen S. A Comparative Study between ECG-based and PPG-based Heart Rate Monitors for Stress Detection. 2022 14th Int Conf Commun Syst NETworkS, COMSNETS 2022. 2022;84–9.
    [26] Sujith AVLN, Sajja GS, Mahalakshmi V, Nuhmani S, Prasanalakshmi B. Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neurosci Informatics [Internet]. 2022;2(3):100028. Available from: https://doi.org/10.1016/j.neuri.2021.100028.
    [27] Yanamala N, Krishna NH, Hathaway QA, Radhakrishnan A, Sunkara S, Patel H, et al. A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients. npj Digit Med [Internet]. 2021;4(1):1 [29] Pinge A, Bandyopadhyay S, Ghosh S, Sen S. A Comparative Study between ECG-based and PPG-based Heart Rate Monitors for Stress Detection. 2022 14th Int Conf Commun Syst NETworkS, COMSNETS 2022. 2022;84–9.
    [28] Nishajith A, Nivedha J, Nair SS, Shaffi JM. Smart Cap – Wearable Visual. 2018 Int Conf Inven Res Comput Appl. 2018;(Icirca):275–8.
    [29] Wang W, Brinker AC Den, Haan G De. Single-Element Remote-PPG. 2019;66(7):2032–43.
    [30] Patel WD, Pandya S, Koyuncu B, Ramani B, Bhaskar S, Ghayvat H. NXTGeUH: Lorawan based next-generation ubiquitous healthcare system for vital signs monitoring. 2018 Conf Inf Commun Technol CICT 2018. 2018;1–8.
    [31] Allado E, Poussel M, Moussu A, Saunier V, Bernard Y, Albuisson E, et al. Innovative measurement of routine physiological variables (heart rate, respiratory rate, and oxygen saturation) using a remote photoplethysmography imaging system: A prospective comparative trial protocol. BMJ Open. 2021;11(8).
    [32] Ramani B, Patel W, Solanki K. Stress Ocare : An advanced IoMT-based physiological data analysis for anxiety status prediction using cloud computing. J Discret Math Sci Cryptogr. 2022;25(4):1019–29.
    [33] Ramani B, Patel W, Solanki K, Patel M. An Energy-Efficient Stress Monitoring System Using Physiological Data. Int J Eng Res Technol. 2021;9(8):132–7.
    [34] Molinaro N, Schena E, Silvestri S, Bonotti F, Aguzzi D, Viola E, et al. Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview. Front Physiol. 2022;13(February).
    [35] Wang Y, Wang W, Zhou M, Ren A, Tian Z. Remote monitoring of human vital signs based on 77-GHZ MM-WAVE FMCW radar. Sensors (Switzerland). 2020;20(10).
    [36] Mudnur SP, Raj Goyal S, Jariwala KN, Patel WD, Ramani B. Hiding the Secret Image Using Two Cover Images for Enhancing the Robustness of the Stego Image Using Haar DWT and LSB Techniques. 2018 Conf Inf Commun Technol CICT 2018. 2018;(2):1–4.
    [37] Maxime Konan DA, Patel W. I-NXGeVita: IoMT based ubiquitous health monitoring system using deep neural networks. 2018 3rd IEEE Int Conf Recent Trends Electron Inf Commun Technol RTEICT 2018 - Proc. 2018;552–7.
    [38] Manic KS, Biju R, Patel W, Khan MA, Raja NSM, Uma S. Extraction and Evaluation of Corpus Callosum from 2D Brain MRI Slice: A Study with Cuckoo Search Algorithm. Comput Math Methods Med. 2021;2021.
    [39] Abdulkareem KH, Mohammed MA, Salim A, Arif M, Geman O, Gupta D, et al. Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment. IEEE Internet Things J. 2021;8(21):15919–28.
    [40] Kangra K, Singh J. Comparative analysis of predictive machine learning algorithms for diabetes mellitus. Bull Electr Eng Informatics. 2023;12(3):1728–37.
    [41] Sankaravadivel V, Thalavaipillai S, Rajeswar S, Ramalingam P. Feature-based analysis of endometriosis using machine learning. Indones J Electr Eng Comput Sci. 2023;29(3):1700–7.
    [42] Kurniawan R, Supardi J, Mohamad FS. A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology. 2023;2023.
    [43] Beltran H, Sansano E, Pecht M. Machine learning techniques suitability to estimate the retained capacity in lithium-ion batteries from partial charge/discharge curves. J Energy Storage [Internet]. 2023;59(September 2022):106346. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2352152X22023350.
    [44] Ramon J, Palacios-marqués D, Ribeiro-Soriano D. Using data mining techniques to explore security issues in smart living environments in Twitter. Comput Commun [Internet]. 2021;179(August):285–95. Available from: https://doi.org/10.1016/j.comcom.2021.08.021.
    [45] Ghaderzadeh M, Asadi F, Hosseini A, Bashash D, Abolghasemi H, Roshanpour A. Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review. Vol. 2021, Scientific Programming. Hindawi Limited; 2021.
    [46] Rehman O, Farrukh Z, Al-Busaidi AM, Oman M, Cha K, Park SJ, et al. IoT Powered Cancer Obs Kothari, Aansi A., et al. "A novel approach towards context-based recommendations using support vector machine methodology." Procedia Computer Science 57 (2015): 1171-1178. Elsevier.
    [47] Kothari, Aansi A., et al. "A novel approach towards context-sensitive recommendations based on machine learning methodology." 2015 Fifth International Conference on Communication Systems and Network Technologies. IEEE, 2015.

Nana Yaw Duodu, Warish D. Patel, Hakan Koyuncu, Felix Nartey and Wisdom Torgby (2024), Empowering Health and Well-being: IoT-Driven Vital Signs Monitoring in Educational Institutions and Elderly Homes Using Machine Learning. IJEER 12(icteee), 40-47. DOI: 10.37391/ijeer.12bdf07.