The Effect of Repeated Measurements Using an Upper Extremity Robot on Healthy Adults

in Journal of Applied Biomechanics
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  • 1 VA Maryland Healthcare System
  • | 2 University of Maryland
  • | 3 Massachusetts Institute of Technology
  • | 4 Cornell University and Burke Medical Research Institute
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We are expanding the use of the MIT-MANUS robotics to persons with impairments due exclusively to orthopedic disorders, with no neurological deficits. To understand the reliability of repeated measurements of the robotic tasks and the potential for registering changes due to learning is critical. Purposes of this study were to assess the learning effect of repeated exposure to robotic evaluations and to demonstrate the ability to detect a change in protocol in outcome measurements. Ten healthy, unimpaired subjects (mean age = 54.1 ± 6.4 years) performed six repeated evaluations consisting of unconstrained reaching movements to targets and circle drawing (with and without a visual template) on the MIT-MANUS. Reaching outcomes were aiming error, mean and peak speed, movement smoothness and duration. Outcomes for circle drawing were axis ratio metric and shoulder–elbow joint angles correlation metric (was based on a two-link model of the human arm and calculated hand path during the motions). Repeated-measures ANOVA (p ≤ .05) determined if difference existed between the sessions. Intraclass correlations (R) were calculated. All variables were reliable, without learning across testing sessions. Intraclass correlation values were good to high (reaching, R ≥ .80; circle drawing, R ≥ .90). Robotic measurement ability to differentiate between similar but distinct tasks was demonstrated as measured by axis ratio metric (p < .001) and joint correlation metric (p = .001). Outcome measures of the MIT-MANUS proved to be reliable yet sensitive to change in healthy adults without motor learning over the course of repeated measurements.

Finley and Bever are with Rehabilitation Research & Development, VA Maryland Healthcare System, and the Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, both in Baltimore, MD; Dipietro is with the Mechanical Engineering Department, Massachusetts Institute of Technology, Cambridge, MA; Ohlhoff and Whitall are with the Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, MD; Krebs is with Mechanical Engineering Department, Massachusetts Institute of Technology, Cambridge, MA, the Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, and the Weill Medical College, Department of Neurology and Neuroscience, Cornell University, Burke Medical Research Institute, White Plains, NY; and Bever is also with the Department of Neurology, University of Maryland School of Medicine, and Neurology and Research Services, VA Maryland Healthcare System, both in Baltimore, MD. Disclosure: Hermano I. Krebs is a co-inventor of the MIT-held patent for the robotic device used in this work and holds equity positions in Interactive Motion Technologies, Inc., a company that manufactures this type of technology under license to MIT.