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

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autores principales: Ren, Tian-Ao, Garcia, Jorge, Hong, Seongheon, Grinberg, Jared, Choi, Hojung, Di, Julia, Li, Hao, Grinberg, Dmitry, Cutkosky, Mark R.
Formato: Preprint
Publicado: 2025
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866911480821055488
author Ren, Tian-Ao
Garcia, Jorge
Hong, Seongheon
Grinberg, Jared
Choi, Hojung
Di, Julia
Li, Hao
Grinberg, Dmitry
Cutkosky, Mark R.
author_facet Ren, Tian-Ao
Garcia, Jorge
Hong, Seongheon
Grinberg, Jared
Choi, Hojung
Di, Julia
Li, Hao
Grinberg, Dmitry
Cutkosky, Mark R.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2512_20992
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Multimodal Sensing for Robot-Assisted Sub-Tissue Feature Detection in Physiotherapy Palpation
Ren, Tian-Ao
Garcia, Jorge
Hong, Seongheon
Grinberg, Jared
Choi, Hojung
Di, Julia
Li, Hao
Grinberg, Dmitry
Cutkosky, Mark R.
Robotics
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.
title Multimodal Sensing for Robot-Assisted Sub-Tissue Feature Detection in Physiotherapy Palpation
topic Robotics
url https://arxiv.org/abs/2512.20992