Optimization Methods for Adaptive and Dynamic Particle ‎Swarms for Accurate Real-Time Localization in Healthcare ‎Settings-A Comprehensive Survey

  • Authors

    • A.H.Jainab Ruxana Ph. D(ECE) Scholar, Department of ECE, Karpagam Academy of Higher Education, Coimbatore, India
    • Dr. S. Malathy HOD/ Professor, Department of ECE, Karpagam Academy of Higher Education, Coimbatore, India‎
    https://doi.org/10.14419/g6ys1k25

    Received date: May 15, 2025

    Accepted date: May 31, 2025

    Published date: July 8, 2025

  • Particle Swarm Optimization (PSO); Healthcare Environment; Real-time Localization

  • Abstract

    Localization in healthcare facilities has the potential to enhance various medical treatments. Therefore, suitable enabling communication ‎technology is necessary for an accurate locating system in this setting. Unfortunately, many problems with commonly used technologies like ‎WiFi, Bluetooth, and RFID make them unsuitable for hospital localization. These problems include expensive implementation costs, weak ‎signals, inaccurate estimates, and possible interference with medical devices. This paper finds new solutions since existing technologies are ‎becoming more expensive to adopt and maintain and are not accurate enough for use in dynamic medical settings. To achieve precise control ‎over the robot's movement, cutting-edge sensors and control systems are combined. The study and creation of a system known as ‎advancements in sensor technology and optimization of control algorithms accompany dynamic accuracy compensation. The fundamental ‎principle behind this paper is to obtain better operational precision by continuously adjusting the controller's settings based on the real-time ‎data gathered by sensors.Particle Swarm Optimization (PSO) improves the model's overall performance by fine-tuning the ‎hyperparameters.When it comes to life-saving equipment, this improved precision is crucial for meeting the requirement for exact ‎positioning in complex and ever-changing healthcare settings. The findings show that PSO makes the model much better, which gives a ‎solid foundation for building high-tech Healthcare localization systems. Compared to more traditional positioning techniques, the suggested ‎localization algorithm outperforms them in terms of localization error and location estimation‎.

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    Ruxana, A. ., & Malathy , D. S. . (2025). Optimization Methods for Adaptive and Dynamic Particle ‎Swarms for Accurate Real-Time Localization in Healthcare ‎Settings-A Comprehensive Survey. International Journal of Basic and Applied Sciences, 14(SI-1), 228-233. https://doi.org/10.14419/g6ys1k25