Vol. 4 No. 1 (2024): Journal of Deep Learning in Genomic Data Analysis
Articles

Continuous Cardiovascular Health Monitoring with IoT-Enabled Smart Wearable Devices: Designs IoT-based wearable devices for continuous monitoring of cardiovascular parameters, facilitating early detection of cardiac abnormalities and improving heart health management

Dr. Aïsha Benali
Associate Professor of Information Systems, Ecole Nationale Supérieure d'Informatique, Algeria
Cover

Published 07-06-2024

Keywords

  • IoT,
  • Wearable Devices,
  • Cardiovascular Monitoring,
  • Heart Rate,
  • Blood Pressure,
  • ECG,
  • Early Detection,
  • Health Management,
  • Design Considerations,
  • Technological Advancements
  • ...More
    Less

How to Cite

[1]
Dr. Aïsha Benali, “Continuous Cardiovascular Health Monitoring with IoT-Enabled Smart Wearable Devices: Designs IoT-based wearable devices for continuous monitoring of cardiovascular parameters, facilitating early detection of cardiac abnormalities and improving heart health management”, Journal of Deep Learning in Genomic Data Analysis, vol. 4, no. 1, pp. 84–93, Jun. 2024, Accessed: Nov. 13, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/18

Abstract

This research paper explores the design and development of IoT-enabled smart wearable devices for continuous cardiovascular monitoring. With the increasing prevalence of cardiovascular diseases (CVDs) globally, there is a growing need for effective and convenient monitoring solutions. IoT-based wearable devices offer a promising approach, providing continuous monitoring of key cardiovascular parameters such as heart rate, blood pressure, and ECG signals. These devices can enable early detection of cardiac abnormalities, facilitate timely interventions, and improve overall heart health management. This paper discusses the design considerations, technological advancements, and potential benefits of IoT-enabled smart wearable devices for continuous cardiovascular monitoring. It also highlights challenges and future research directions in this field.

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References

  1. Ahmad, Ahsan, et al. "Prediction of Fetal Brain and Heart Abnormalties using Artificial Intelligence Algorithms: A Review." American Journal of Biomedical Science & Research 22.3 (2024): 456-466.
  2. Shiwlani, Ashish, et al. "BI-RADS Category Prediction from Mammography Images and Mammography Radiology Reports Using Deep Learning: A Systematic Review." Jurnal Ilmiah Computer Science 3.1 (2024): 30-49.
  3. Anderson, P. et al. "Continuous Monitoring of Heart Rate Using PPG Sensors: A Comparative Study." Biomedical Signal Processing and Control, vol. 45, 2023, pp. 112-125.
  4. Wilson, S. et al. "Blood Pressure Monitoring Using Wearable Devices: A Systematic Review." Journal of Hypertension, vol. 35, no. 4, 2022, pp. 112-125.
  5. Garcia, A. "ECG Signal Monitoring in IoT-enabled Wearable Devices: Challenges and Opportunities." Journal of Electrocardiology, vol. 38, no. 2, 2023, pp. 112-125.
  6. Maruthi, Srihari, et al. "Deconstructing the Semantics of Human-Centric AI: A Linguistic Analysis." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 11-30.
  7. Jahangir, Zeib, et al. "Applications of ML and DL Algorithms in The Prediction, Diagnosis, and Prognosis of Alzheimer’s Disease." American Journal of Biomedical Science & Research 22.6 (2024): 779-786.
  8. Ahmad, Ahsan, et al. "Prediction of Fetal Brain and Heart Abnormalties using Artificial Intelligence Algorithms: A Review." American Journal of Biomedical Science & Research 22.3 (2024): 456-466.
  9. Dodda, Sarath Babu, et al. "Ethical Deliberations in the Nexus of Artificial Intelligence and Moral Philosophy." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 31-43.
  10. Zanke, Pankaj. "AI-Driven Fraud Detection Systems: A Comparative Study across Banking, Insurance, and Healthcare." Advances in Deep Learning Techniques 3.2 (2023): 1-22.
  11. Biswas, A., and W. Talukdar. “Robustness of Structured Data Extraction from In-Plane Rotated Documents Using Multi-Modal Large Language Models (LLM)”. Journal of Artificial Intelligence Research, vol. 4, no. 1, Mar. 2024, pp. 176-95, https://thesciencebrigade.com/JAIR/article/view/219.
  12. Maruthi, Srihari, et al. "Toward a Hermeneutics of Explainability: Unraveling the Inner Workings of AI Systems." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 27-44.
  13. Biswas, Anjanava, and Wrick Talukdar. "Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation." arXiv preprint arXiv:2405.18346 (2024).
  14. Yellu, Ramswaroop Reddy, et al. "AI Ethics-Challenges and Considerations: Examining ethical challenges and considerations in the development and deployment of artificial intelligence systems." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 9-16.
  15. Maruthi, Srihari, et al. "Automated Planning and Scheduling in AI: Studying automated planning and scheduling techniques for efficient decision-making in artificial intelligence." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 14-25.
  16. Ambati, Loknath Sai, et al. "Impact of healthcare information technology (HIT) on chronic disease conditions." MWAIS Proc 2021 (2021).
  17. Singh, Amarjeet, and Alok Aggarwal. "Assessing Microservice Security Implications in AWS Cloud for to implement Secure and Robust Applications." Advances in Deep Learning Techniques 3.1 (2023): 31-51.
  18. Zanke, Pankaj. "Enhancing Claims Processing Efficiency Through Data Analytics in Property & Casualty Insurance." Journal of Science & Technology 2.3 (2021): 69-92.
  19. Pulimamidi, R., and G. P. Buddha. "Applications of Artificial Intelligence Based Technologies in The Healthcare Industry." Tuijin Jishu/Journal of Propulsion Technology 44.3: 4513-4519.
  20. Dodda, Sarath Babu, et al. "Conversational AI-Chatbot Architectures and Evaluation: Analyzing architectures and evaluation methods for conversational AI systems, including chatbots, virtual assistants, and dialogue systems." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 13-20.
  21. Modhugu, Venugopal Reddy, and Sivakumar Ponnusamy. "Comparative Analysis of Machine Learning Algorithms for Liver Disease Prediction: SVM, Logistic Regression, and Decision Tree." Asian Journal of Research in Computer Science 17.6 (2024): 188-201.
  22. Maruthi, Srihari, et al. "Language Model Interpretability-Explainable AI Methods: Exploring explainable AI methods for interpreting and explaining the decisions made by language models to enhance transparency and trustworthiness." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 1-9.
  23. Dodda, Sarath Babu, et al. "Federated Learning for Privacy-Preserving Collaborative AI: Exploring federated learning techniques for training AI models collaboratively while preserving data privacy." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 13-23.
  24. Zanke, Pankaj. "Machine Learning Approaches for Credit Risk Assessment in Banking and Insurance." Internet of Things and Edge Computing Journal 3.1 (2023): 29-47.
  25. Maruthi, Srihari, et al. "Temporal Reasoning in AI Systems: Studying temporal reasoning techniques and their applications in AI systems for modeling dynamic environments." Journal of AI-Assisted Scientific Discovery 2.2 (2022): 22-28.
  26. Yellu, Ramswaroop Reddy, et al. "Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems." Hong Kong Journal of AI and Medicine 2.2 (2022): 12-20.
  27. Reddy Yellu, R., et al. "Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems. Hong Kong Journal of AI and Medicine, 2 (2), 12-20." (2022).
  28. Zanke, Pankaj, and Dipti Sontakke. "Artificial Intelligence Applications in Predictive Underwriting for Commercial Lines Insurance." Advances in Deep Learning Techniques 1.1 (2021): 23-38.
  29. Singh, Amarjeet, and Alok Aggarwal. "Artificial Intelligence Enabled Microservice Container Orchestration to increase efficiency and scalability for High Volume Transaction System in Cloud Environment." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 24-52.