Ultrasound-Guided Assistive Robots for Scoliosis Assessment with Optimization-based Control and Variable Impedance

TL;DR

This paper presents an optimization-based control framework for an ultrasound-guided assistive robot to automate scoliosis assessment, integrating variable impedance regulation learned from experts to ensure safety and accuracy during human-robot interaction.

Contribution

It introduces a novel control framework combining quadratic programming with variable impedance regulation learned from demonstrations for autonomous scoliosis assessment.

Findings

Successful real-world experiments with phantom and human subjects

High-quality coronal spinal images obtained autonomously

Enhanced safety and stability during spine scanning

Abstract

Assistive robots for healthcare have seen a growing demand due to the great potential of relieving medical practitioners from routine jobs. In this paper, we investigate the development of an optimization-based control framework for an ultrasound-guided assistive robot to perform scoliosis assessment. A conventional procedure for scoliosis assessment with ultrasound imaging typically requires a medical practitioner to slide an ultrasound probe along a patient's back. To automate this type of procedure, we need to consider multiple objectives, such as contact force, position, orientation, energy, posture, etc. To address the aforementioned components, we propose to formulate the control framework design as a quadratic programming problem with each objective weighed by its task priority subject to a set of equality and inequality constraints. In addition, as the robot needs to establish constant contact with the patient during spine scanning, we incorporate variable impedance regulation of the end-effector position and orientation in the control architecture to enhance safety and stability during the physical human-robot interaction. Wherein, the variable impedance gains are retrieved by learning from the medical expert's demonstrations. The proposed methodology is evaluated by conducting real-world experiments of autonomous scoliosis assessment with a robot manipulator xArm. The effectiveness is verified by the obtained coronal spinal images of both a phantom and a human subject.