Advance Quantum Wavelet Transformation-based intelligent system for Personalized Nursing Care Using IoT Devices

The advanced development of the Internet of Things (IoT) technology has made it possible to monitor health conditions continuously and make decisions based on data in individual nursing practice. Nevertheless, biomedical signals are multidimensional, nonstationary and complex, which creates great challenges to traditional data analytics. To deal with this, we suggest an Advance Quantum Wavelet Transformation (AQWT)-based smart analytics model, which incorporates IoT-based health tracking with quantum-inspired wavelet operations to provide accurate and responsive nursing care. Physiological parameters (heart rate, blood oxygen level, body temperature and respiratory activity) are gathered in real-time by the wearable IoT devices employed in the framework. To break down and examine such signals within a variety of resolutions, AQWT is used, which allows detecting small anomalies and enhances the interpretability of patient health status. The data that have been processed are then combined with machine learning models to produce custom nursing advice, early risk anticipation and patient-centered care planning. Through experimental analysis, it can be shown that the AQWT-based framework can provide more accurate predictions, minimize the computational load by efficiently compressing data and enable privacy-preserving information transmission over IoT networks. This paper presents the opportunities of AQWT-based intelligent analytics to change personalized nursing care into a proactive, accurate and patient-centered health model.