Multimodal Sensing for Robot-Assisted Sub-Tissue Feature Detection in Physiotherapy Palpation

TL;DR

This paper introduces a multimodal sensor combining tactile imaging and force sensing to improve subsurface feature detection during robot-assisted physiotherapy palpation, demonstrating enhanced robustness and accuracy.

Contribution

A novel compact multimodal sensor integrating tactile imaging with force sensing for improved subsurface feature detection in robotic palpation.

Findings

Tactile images reveal clear structural differences in phantom experiments.

Force signals alone often produce ambiguous responses.

Combining modalities enables robust detection and controlled palpation.

Abstract

Robotic palpation relies on force sensing, but force signals in soft-tissue environments are variable and cannot reliably reveal subtle subsurface features. We present a compact multimodal sensor that integrates high-resolution vision-based tactile imaging with a 6-axis force-torque sensor. In experiments on silicone phantoms with diverse subsurface tendon geometries, force signals alone frequently produce ambiguous responses, while tactile images reveal clear structural differences in presence, diameter, depth, crossings, and multiplicity. Yet accurate force tracking remains essential for maintaining safe, consistent contact during physiotherapeutic interaction. Preliminary results show that combining tactile and force modalities enables robust subsurface feature detection and controlled robotic palpation.