Radio Frequency (RF) sensing offers a completely novel and non-contact approach by exploiting RF reflections from, and through, the body for detecting small respiratory motions. The current study has used RF-based Software Defined Radio Frequency (SDRF) sensing to detect various breathing rates, including fast, normal, and shortness of breath. A correlation matrix and standard deviation analysis of 146 OFDM (Orthogonal Frequency Division Multiplexing) subcarriers was performed to determine signal variability and consistency. Machine learning-based results subsequently show that cleaned data improve subcarrier correlation uniformity, with an enhanced focus on normal breathing, while still maintaining signal features related to all breathing modes. Subcarrier selection, filtering and normalization strengthen the accuracy of the obtained data as the preprocessing stages eliminate the noises and artifacts. This paper demonstrates the reliability of Radio Frequency (RF)-based systems for respiratory monitoring, as well as the possibility of extracting highly detailed features relevant to developing more complex, real-time healthcare solutions.
Qurat Ul Ain, R. Asif, Nan Zhao et al.· E3S Web of Conferences· 0 citations
This paper presents an Intrusion Detection System (IDS) grounded in Explainable Artificial Intelligence (XAI) to enhance transparency, reliability, and user trust in IoT security. To make detection decisions interpretable and accountable, the system employs ensemble machine learning for real-time anomaly detection and integrates two complementary explainability methods: SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). A Digital Twin (DT) module continuously mirrors device behaviour, supporting predictive threat analysis and early anomaly identification by detecting deviations from expected operational baselines. The framework is evaluated on the TON_IoT benchmark dataset using accuracy, precision, recall, F1-score, ROC-AUC, and the Matthews Correlation Coefficient (MCC). Experimental results demonstrate that Random Forest and XGBoost achieve the highest accuracy of 0.996. In the XAI comparison, SHAP outperforms LIME across all metrics $(\mathbf{F} \mathbf{1} \boldsymbol{=} \mathbf{0. 9 9 5}$, $\mathbf{R O C}-\mathbf{A U C} \boldsymbol{=} \mathbf{0. 9 9 8}$ vs. $\mathbf{0. 9 9 3}$ for LIME), confirming its stronger explanatory and predictive effectiveness. While the current framework focuses on XAIdriven detection and digital twin integration, the architecture is designed to accommodate future extensions, including blockchain with zero-knowledge proof (ZKP) protocols for tamper.
Ohood Alharbi, R. Shaikh, Raheel Hassan et al.· International Conference on...· 0 citations
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