Alternative healthcare solutions have been identified as a viable approach to ameliorate the increasing demand for telehealth and prompt healthcare delivery. Moreover, indoor ocalization using different technologies and approaches have greatly contributed to alternative healthcare solutions. In this paper, a cost-effective, radio frequency identification (RFID)-based indoor location system that employs received signal strength (RSS) information of passive RFID tags is presented. The proposed system uses RFID tags placed at different positions on the target body. The mapping of the analysed data against a set of reference position datasets is used to accurately track the vertical and horizontal positioning of a patient within a confined space in real-time. The Euclidean distance model achieves an accuracy of 98% for all sampled activities. However, the accuracy of the activity recognition algorithm performs below the threshold performance for walking and standing, which is due to similarities in the target height, weight and body density for both activities. The obtained results from the proposed system indicate significant potentials to provide reliable health measurement tool for patients at risk.

Reconfigurable intelligent surfaces (RIS) provide a compact and low-loss platform for controlling electromagnetic phase responses. This study investigates spoof plasmonic, varactor-loaded RIS unit cells as an approach for achieving continuous and broadband phase tunability within a simple and scalable architecture. A varactor-diode-loaded spoof surface plasmon polariton (SSPP) unit cell is designed for wideband reconfigurable operation in sub-5.9 GHz wireless sensing and communication systems. The proposed multilayer structure incorporates bias-controlled varactors at regions of strong electromagnetic field confinement within a spoof plasmonic cavity, enabling a continuous 180° phase tuning with reflection loss below − 10 dB over the 2–11.85 GHz frequency range. The unit cell exhibits a compact normalized footprint of 0.20λ₀ × 0.20λ₀ and maintains a |Γ| variation below 0.4 dB across four hybrid bias states, indicating nearly constant reflection magnitude and consistent field confinement under reconfiguration. These characteristics demonstrate broadband phase reconfigurability and support the applicability of the proposed RIS unit cell for electromagnetic wavefront manipulation in wireless sensing and channel control.

This study presents a deep learning-enabled framework for non-contact respiratory pattern classification using software-defined radio frequency sensing. The proposed approach builds on an SDR-based wireless channel state information acquisition system to identify three breathing patterns: normal breathing, fast breathing, and shortness of breath. To improve signal quality and class separability, the raw WCSI (wireless channel state information) data were processed through a structured denoising pipeline comprising wavelet-based filtering, DC component removal, low-variance subcarrier elimination, and normalization. Correlation matrix and power spectral density analyses were then used to compare raw and cleaned signals, demonstrating that preprocessing substantially enhances the visibility of breathing-induced channel variations. DNN (Deep Neural Network) and RNN (Recurrent Neural Network) models were trained and evaluated using five-fold cross-validation. The cleaned data produced consistently high classification performance, with both models’ achieving accuracy above 98%, while the DNN showed marginally superior precision, recall, and F1-score. The DNN confusion matrix further demonstrated near-complete separation among the three breathing classes, confirming the effectiveness of the preprocessing and feature extraction strategy. A multi-attribute decision-making analysis (MADM) based on the SAW (Simple Additive Weighting method) was also introduced to rank model performance across multiple evaluation metrics, identifying the cleaned DNN as the most robust configuration. The results confirm that SDRF (software-defined radio frequency) sensing combined with signal conditioning and deep learning can provide a low-cost, scalable, and contactless solution for respiratory monitoring. This framework offers promising potential for smart healthcare applications, including sleep monitoring, respiratory disease screening, and continuous patient observation in environments where wearable or contact-based sensors may be unsuitable.

Among the commonly occurring medical emergencies such as heart attack, arrhythmia, valve diseases and high blood pressure, reduction in blood flow to the heart prevents the reception of sufficient oxygen to the heath muscles known as myocardial ischemia caused the partial of complete blockage of coronary arteries. Myocardial ischemia prevents the heart muscles to pump the blood efficiently that can ultimately lead to the heart attack or may cause abnormal heart rhythms. Electrocardiogram (ECG) an electrical activity of the heart is mostly used by the cardiologist for diagnosing MI patients. Identifying the MI manually is time consuming and a possibility of misinterpretation exists to the changes in the patient ECG. An automated method for the detection of MI pattern in the ECG is proposed using wavelet transform. It is observed that the difference in the height of PR segment and J-point allows to distinguish between a normal ECG and abnormal MI ECG. Furthermore, this study also finds that there is a significant difference in the J-point, R-peak amplitude and ST- wave of the MI patient than the normal healthy person when the patients ECG is recorded using MAX30001.