Compliance-Based Sensor Placement for Force Sensing on a Sensorized Prostate Phantom

2026-06-05

AI Summary

This study addresses the challenge of optimizing sparse sensor placement on prostate phantoms for digital rectal examination training. The authors propose a region-aware weighted greedy strategy that prioritizes sensor deployment in the clinically critical posterior contact region while avoiding non-relevant areas, under strict constraints on sensor count. The approach constructs a force-response compliance matrix through finite element simulation and validates its efficacy via QR decomposition-based comparison. Experimental results demonstrate that, compared to a global QR-based layout, the proposed method achieves a 22.5% improvement in average force reconstruction accuracy within the target region, significantly enhancing localized contact force perception while effectively balancing clinical relevance and sensing performance.

Abstract

This work presents a compliance-based sensor placement method for force sensing on a sensorized prostate phantom designed for Digital Rectal Examination training. The phantom combines three internal pneumatic chambers, used as intrinsic pressure sensors, with ten surface displacement markers. A finite-element simulation dataset is generated by applying external forces at sampled surface locations, from which a compliance matrix relating force inputs to pressure and displacement responses is constructed. Based on this matrix, we propose a weighted greedy selection strategy that maximizes local force reconstructability while prioritizing the clinically relevant posterior contact region and avoiding marker placement directly within the Region of Interest. Compared with a global QR-based placement strategy, the proposed method increases the mean reconstructability score in the target region by 22.5%. These results suggest that region-aware sparse sensor placement can improve force observability in soft robotic medical phantoms while maintaining a limited and practical sensing configuration.

Problem

• sensor placement
• force sensing
• prostate phantom
• compliance
• medical training

Innovation

• compliance-based sensor placement
• force reconstructability
• region-aware sensing
• soft robotic phantom
• sparse sensor optimization