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Modeling, Control, and Clinical Validation of an Upper-limb Medical Education Task Trainer for Elbow Spasticity and Rigidity Assessment
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  • Yinan Pei ,
  • Mahshid Mansouri ,
  • Christopher M. Zallek ,
  • Elizabeth Hsiao-Wecksler
Yinan Pei
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Mahshid Mansouri
University of Illinois at Urbana-Champaign, University of Illinois at Urbana-Champaign

Corresponding Author:[email protected]

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Christopher M. Zallek
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Elizabeth Hsiao-Wecksler
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Abstract

The goal of this study was to validate a series elastic actuator (SEA)-based robotic arm that can mimic three abnormal muscle behaviors, namely lead-pipe rigidity, cogwheel rigidity, and spasticity for medical education training purposes. Key characteristics of each muscle behavior were first modeled mathematically based on clinically-observed data across severity levels. A controller that incorporated feedback, feedforward, and disturbance observer schemes was implemented to deliver haptic target muscle resistive torques to the trainee during passive stretch assessments of the robotic arm. A series of benchtop tests across all behaviors and severity levels were conducted to validate the torque estimation accuracy of the custom SEA (RMSE: ~ 0.16 Nm) and the torque tracking performance of the controller (torque error percentage: < 2.8 %). A clinical validation study was performed with seven experienced clinicians to collect feedback on the task trainer’s simulation realism via a Classification Test (CT) and a Disclosed Assessment Test (DAT). In the CT, subjects were able to classify different muscle behaviors with a mean accuracy > 87 % and could further distinguish severity level within each behavior satisfactorily. In the DAT, subjects generally agreed with the simulation realism and provided suggestions on haptic behaviors for future iterations. Overall, subjects scored 4.9 out of 5 for the potential usefulness of this device as a medical education tool for students to learn spasticity and rigidity assessment.
2023Published in IEEE Transactions on Neural Systems and Rehabilitation Engineering volume 31 on pages 3320-3330. 10.1109/TNSRE.2023.3304951