Authors : Sofi Showkat Saleem; Dr. Zahid Abdul Majeed; Dr. Qumail Hussain; Suhail Anjum Rather

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/2dpfm5et

Scribd : https://tinyurl.com/pku4fv9d

DOI : https://doi.org/10.38124/ijisrt/26jul398

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Current intensive care infrastructure relies heavily on tethered fiberoptic networks that are susceptible to physical degradation, compromising data integrity and patient safety. bility (Diomidous et al., 2020). Furthermore, these wireless platforms resolve critical limitations of conventional patient monitors, such as excessive auditory alarm fatigue and the persistent requirement for continuous visual proximity to bedside interfaces (Andrade et al., 2020, p. 1). By enabling the seamless transmission of physiological data via wearable sensors, these systems facilitate enhanced alarm response rates and significantly reduce the time required for clinical interventions (Ma et al., 2025, p. 341; Nathan, 2019). Moreover, these wearable devices enable the continuous, non-invasive acquisition of vital signs, bridging the monitoring gap between intensive care units and standard hospital wards (Angelucci et al., 2025; Chen, 2025). Despite these advancements, widespread adoption requires rigorous clinical validation to ensure that signal fidelity remains robust against the electromagnetic interference common in high-acuity environments (Kotamarthy et al., 2025; Leenen et al., 2020, p. 1). Advanced data-processing algorithms must be integrated into these architectures to distinguish physiological signals from ambient environmental noise, ensuring the reliability necessary for life-critical decision-making (Murali et al., 2020, p. 1; Xu et al., 2021, p. 394). Furthermore, the integration of these systems into existing electronic health records remains a logistical hurdle that requires standardized interoperability protocols to support informed clinical decision-making (Bhalsod et al., 2025).

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