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<p>Modern healthcare systems (particularly those with limited resources and in remote locations) require real-time monitoring and telemedicine. Conventional cloud-reliant solutions face challenges with latency, data privacy, and trust. The system integrates a CNN-GRU hybrid deep learning model for classifying physiological signals, a Trust-Aware Diagnostic Engine based on EMA scoring, and a Federated Learning system augmented with Differential Privacy (DP) to ensure decentralized training. The system was extensively tested on PhysioNet MIT-BIH, MIMIC-III, and a simulated H-IoT dataset to assess real-time performance, accuracy, and efficiency. The proposed CNN-GRU model outperformed the baseline models (LSTM, Transformer-only) in average accuracy (96.3), F1-score (0.94), and ROC-AUC (0.97). In the hybrid edge-fog, latency was reduced by 28%. When device signal noise was intermittent, the trust score remained stable at over 0.85. Under DP noise (epsilon = 0.1), the federated learning model converged in 40 rounds with a negligible loss of accuracy. SHAP feature attribution made the predictions interpretable. The framework presents a new combination of federated learning, privacy-preserving and trust-based decision-making, and adaptive edge-fog orchestration for smart healthcare. This work provides a standard for the design of clinically relevant, safe, and interpretable systems based on AI-powered telemedicine and strongly aligns with translational impact.</p>

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Design and optimisation of an IoT-based artificial intelligence framework for real-time health monitoring and telemedicine diagnostics in smart healthcare systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Nithesh Naik,
  • Nikhil Kassetty,
  • Srinivas Chippagiri,
  • Princy Randhawa,
  • B. M. Zeeshan Hameed,
  • Vathsala Patil

摘要

Modern healthcare systems (particularly those with limited resources and in remote locations) require real-time monitoring and telemedicine. Conventional cloud-reliant solutions face challenges with latency, data privacy, and trust. The system integrates a CNN-GRU hybrid deep learning model for classifying physiological signals, a Trust-Aware Diagnostic Engine based on EMA scoring, and a Federated Learning system augmented with Differential Privacy (DP) to ensure decentralized training. The system was extensively tested on PhysioNet MIT-BIH, MIMIC-III, and a simulated H-IoT dataset to assess real-time performance, accuracy, and efficiency. The proposed CNN-GRU model outperformed the baseline models (LSTM, Transformer-only) in average accuracy (96.3), F1-score (0.94), and ROC-AUC (0.97). In the hybrid edge-fog, latency was reduced by 28%. When device signal noise was intermittent, the trust score remained stable at over 0.85. Under DP noise (epsilon = 0.1), the federated learning model converged in 40 rounds with a negligible loss of accuracy. SHAP feature attribution made the predictions interpretable. The framework presents a new combination of federated learning, privacy-preserving and trust-based decision-making, and adaptive edge-fog orchestration for smart healthcare. This work provides a standard for the design of clinically relevant, safe, and interpretable systems based on AI-powered telemedicine and strongly aligns with translational impact.