A Miniature Wearable Ultrasound System for Continuous Bladder Monitoring with Sleeping-Position-Robust Modeling Strategies.

Abstract

Wearable ultrasound bladder monitoring often assumes static body positions, which is unrealistic for all-day usage. This work aims to enable reliable, non-invasive bladder-fullness classification by mitigating performance degradation caused by sleeping position variations. A miniature wearable ultrasound system for bladder monitoring has been developed. It weighs 32.2 gram (inc. battery) and draws an average current of 8.59 mA, supporting long-term operation. To ensure robustness to sleeping position variations, three modeling strategies were investigated: (i) Pooled training on multi-sleeping-position data, (ii) Oracle routing to sleeping-position-specific models based on dual-modal data, and (iii) Adversarial training using a confounder-free network (CF-Net) to achieve sleeping-position-independent feature extraction. The system has been validated on 12 participants across diverse sleeping positions. The baseline model trained on specific sleeping positions achieved only 75.95% average accuracy when tested on unseen position samples. Relative to this baseline, after applying the Pooled, Oracle and CF-Net strategies, classification accuracy improved by 16.48%, 20.03% and 18.56%, respectively. The proposed system enabled wearable ultrasound bladder monitoring with substantially improved robustness to sleeping-position variations. This study is the first to address the challenge of sleeping-position variation in wearable bladder monitoring, demonstrating its feasibility of all-day, home-based monitoring with clinically meaningful pre-void alerts.