Real-Time WBAN Monitoring: An Adaptive Framework for Selective Signal Restoration and Physiological Trend Prediction.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Health Informatics

Background:

• Wireless Body Area Networks (WBANs) are crucial for real-time health monitoring but face challenges like sensor degradation, noise, and low-latency requirements.
• Existing preprocessing methods often process all data, increasing computational load and latency, and potentially distorting physiological signals.

Purpose of the Study:

• To develop a unified, real-time monitoring framework for WBANs that enhances data integrity and reduces computational overhead.
• To improve the reliability and efficiency of physiological monitoring in resource-limited WBAN environments.

Main Methods:

• Implemented an adaptively gated multi-stage preprocessing pipeline for selective data restoration.
• Utilized an overlap-aware sliding-window mechanism for low-latency processing.
• Integrated a clinically informed risk assessment strategy for early-warning support.

Main Results:

• Achieved 53-67% Mean Squared Error reduction in signal reconstruction under degradation.
• Forecasting layer demonstrated 65-70% directional accuracy for 10-second ahead predictions.
• Aggregated multi-modal risk decision showed 0.89 sensitivity and 0.92 specificity.

Conclusions:

• The proposed framework offers a robust, low-latency, and computationally efficient solution for dependable WBAN physiological monitoring.
• Selective data processing optimizes resource utilization and maintains physiological signal integrity.
• The system provides reliable early-warning capabilities for clinical intervention.