Intuitive Human-in-the-Loop Control for Medical Cyber-Physical Systems

Abstract:

Introduction:

Human-in-the-loop cyber-physical systems should be intuitive and natural to use, especially in complex environments, such as the operating room, and in the presence of difficult system properties, such as nonholonomic kinematics. However, what constitutes an intuitive control interface between a human operator and physical system is not always clear. For example, some nonholonomic systems (e.g. bicycles, cars) seem best controlled in joint space, where the user controls system inputs, such as steering angle and velocity. Recently, the robotics literature has shown that kinematically-similar systems (i.e. steerable needles, wheelchairs) are better controlled in Cartesian space, where the user controls a desired system output, such as position. For these systems, the addition of a cyber control layer between the operator and the robot enables exploration of novel teleoperation mappings, which may lead to more intuitive human-in-the-loop control. The goal of this project is to quantify measures of intuitiveness or naturalness, which may then be used to validate novel human control paradigms for cyber-physical systems.

Poster Summary:

We have begun to integrate a variety of physiological sensors (EEG, EMG, galvanic skin response, heart rate, etc.) with custom C++ code and the Robot Operating System (ROS) to control a haptic device in different teleoperation control scenarios, while recording user performance and physiological response. For the first phase of this project, we design a task of known difficulty using Fitts' Law, a psychomotor law that relates movement time to the width and separation distances of two targets. Our preliminary human subjects study (UTD IRB #14-57) shows clear changes in user performances based on our experimental setup and we are now starting to collect and analyze physiological data for this task. This analysis will lead to metrics for the intuitiveness of a control task. For the second phase of this project, our intuitiveness metrics will be used to evaluate task of unknown difficulty, such as the teleoperation of steerable needles. As part of an REU supplement project, we have begun to identify important muscle groups and muscle calibration procedures for three different needle steering teleoperation algorithms: joint space, Cartesian space, and Cartesian space with force feedback. We have also recently conducted a preliminary pilot study with 6 human subjects while collecting EMG, EEG, galvanic skin response, heart rate, and objective performance metrics for each of the algorithms and are analyzing the results.

Acknowledgements:

This workshop was supported by NSF CRII CPS 1464432 and an REU Supplement