Session: 05-12-01: Robotics, Rehabilitation - I

Paper Number: 93997

93997 - Type X Robot: Theory and Practice for a Revolution in Motor Learning 

This research proposes and tests the concept of using a robot to train a subject to perform a desired repetitive motion, wherein the level of the assist of the robot fades away as the subject learns the motion.  The proposed robot is designated as Type X where the “X” in represents the variable level of autonomy.  A Type X robot has been built and tested, with the results suggesting that transient learning using the Type X provides better training of a repetitive task than was achieved through purely active learning (no robot assist) or purely passive learning (the robot is in full control of the motion during training). 

The research proposes a common framework for defining the State of Autonomy (SOA) of a robot. Unlike most robots that have a static SOA, for a Type X robot the SOA is time varying. Unlike most robotic systems that strive toward increasing autonomy over time, the Type X robot is designed to eliminate itself over time, not eliminate the need for the human over time.  The remainder of this research speaks to the development of a Type X robot and its use in training subjects to perform a desired repetitive bimanual movement.

A computer-controlled DC motor driven Type X robot hardware and software system was designed and constructed in order to test the hypothesis that transient learning would outperform both active and passive learning. The robot consists of two levers, each connected to a DC torque motor. The desired motion involves having the subject need move the two levers with sinusoidal motion of amplitude 30 degrees and with the right lever at twice the frequency of the left lever. A TV and speaker system provides the visual (the subject can see the trace created by the lever motion, where left lever controls the cursor’s horizontal position on the screen, and the right lever controls the cursor’s horizontal motion on the screen) and auditory feedback signals (essentially a metronome to establish the desired frequency) needed for the subject to trace a desired Lissajous curve (resembling a figure “8”).

The Type X robot was tested by randomly assigning 15 subjects into three different groups: active learning (zero robot assist), passive learning (complete robot assist), and transient learning (dynamic robot assist progressing from complete at the start of training towards no assist at the completion of training).  Each subject underwent 90 minutes of training per day (broken into six segments of 15 minutes each) on each of five different days.  At the end of each 15-minute training segment, an assessment test (with no assist from the robot) was conducted to assess how well the desired motion was being learned.  The motion data during each assessment was collected to allow comparison of the various subjects’ performances on drawing the desired Lissajous curve over the duration of their 450 minutes of training.  A series of metrics were developed to enable comprehensive comparison of the subjects’ performances. MATLAB and Minitab are the main data analyzing tools.

The results of the research demonstrate that subjects in the transient learning group demonstrated clear and statistically significant performance increases in comparison to both the active learning and passive learning groups.  One additional subject was tested with transient learning using a more adaptive approach, wherein the trainer (the first author) closely observed the subjects’ performance on each assessment test and made a decision as to what level of autonomy to use during the next training session. This subject outperformed the other 15 subjects, strongly suggesting potential future work in this regard.

The implications of this research are potentially profound and far-reaching, with applications including rehabilitation, sports training, etc.

Presenting Author: Danqing Zhang University of Detroit Mercy

Presenting Author Biography: Ph.D. in Mechanical Engineering from University of Detroit Mercy

Authors:

Danqing Zhang University of Detroit Mercy
Jonathan Weaver University of Detroit Mercy