Published 09-09-2024
Keywords
- IoT,
- intervention
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
This paper explores the design and implementation of IoT-enabled smart monitoring systems for neonatal intensive care units (NICUs). Neonatal care, especially for premature infants, requires continuous monitoring and precise interventions to ensure optimal outcomes. Traditional NICU monitoring systems often lack the ability to provide real-time data analytics and remote access, limiting their effectiveness in managing critical cases. IoT technologies offer a promising solution by integrating sensors, data analytics, and communication protocols to create interconnected monitoring systems. These systems can enhance patient monitoring, facilitate early intervention, and improve overall care delivery in NICUs. This paper presents a comprehensive overview of the design considerations, architecture, and potential benefits of IoT-enabled smart monitoring systems in NICUs. Additionally, it discusses the challenges and future directions for implementing these systems in clinical settings.
Downloads
References
- Saeed, A., Zahoor, A., Husnain, A., & Gondal, R. M. (2024). Enhancing E-commerce furniture shopping with AR and AI-driven 3D modeling. International Journal of Science and Research Archive, 12(2), 040-046.
- Biswas, Anjanava, and Wrick Talukdar. "Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)." Journal of Science & Technology 4.6 (2023): 55-82.
- N. Pushadapu, “Artificial Intelligence for Standardized Data Flow in Healthcare: Techniques, Protocols, and Real-World Case Studies”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 435–474, Jun. 2023
- Chen, Jan-Jo, Ali Husnain, and Wei-Wei Cheng. "Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional Computer Vision." Proceedings of SAI Intelligent Systems Conference. Cham: Springer Nature Switzerland, 2023.
- Alomari, Ghaith, et al. “AI-Driven Integrated Hardware and Software Solution for EEG-Based Detection of Depression and Anxiety.” International Journal for Multidisciplinary Research, vol. 6, no. 3, May 2024, pp. 1–24.
- Saeed, Ayesha, et al. "A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 1-9.
- Pelluru, Karthik. "Integrate security practices and compliance requirements into DevOps processes." MZ Computing Journal 2.2 (2021): 1-19.