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A Miniature Wearable Ultrasound System for Continuous Bladder Monitoring with Sleeping-Position-Robust Modeling
IEEE Transactions on Bio-Medical Engineering
|June 25, 2026
Summary
This study developed a wearable ultrasound bladder monitor that accurately tracks fullness despite sleeping position changes. The system offers reliable, all-day home monitoring with improved robustness for pre-void alerts.
Area of Science:
- Biomedical Engineering
- Wearable Technology
- Medical Devices
Background:
- Wearable ultrasound bladder monitoring systems often fail with dynamic body positions.
- All-day usage requires systems robust to variations like sleeping positions.
Purpose of the Study:
- To develop a wearable ultrasound system for reliable bladder fullness classification.
- To mitigate performance degradation caused by sleeping position variations.
Main Methods:
- A miniature wearable ultrasound system (32.2g, 8.59mA avg. current) was created.
- Three strategies were investigated for robustness: pooled training, oracle routing, and adversarial training (CF-Net).
- The system was validated on 12 participants across diverse sleeping positions.
Main Results:
- The baseline model achieved 75.95% accuracy on unseen positions.
- Pooled, Oracle, and CF-Net strategies improved accuracy by 16.48%, 20.03%, and 18.56%, respectively.
- The CF-Net approach demonstrated sleeping-position-independent feature extraction.
Conclusions:
- The proposed system significantly enhances wearable ultrasound bladder monitoring robustness to sleeping position variations.
- This research enables feasible all-day, home-based bladder monitoring with clinically relevant pre-void alerts.
