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Learning and Reproduction of Therapist’s Semi-Periodic Motions during Robotic Rehabilitation

Published online by Cambridge University Press:  21 May 2019

Carlos Martinez  and
Mahdi Tavakoli
Carlos Martinez*
Affiliation:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada
Mahdi Tavakoli
Affiliation:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada
*
*Corresponding author. E-mail: [email protected]
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Summary

The demand for rehabilitation services has increased in recent years due to population aging. Due to the limitations of therapist’s time and healthcare resources, robot-assisted rehabilitation is becoming an appealing, powerful, and economical solution. In this paper, we propose a solution that combines Learning from Demonstration (LfD) and robotic rehabilitation to save the therapist’s time and reduce the therapy costs when the therapy involves periodic or semi-periodic motions.We begin by modeling the therapist’s behavior (a periodic or semi-periodic motion) using a Fourier Series (FS). Later, when the therapist is no longer involved, the system reproduces the learned behavior modeled by the FS using a robot. A second goal is to combine the above with Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) to obtain a more flexible and generalizable reproduction of the therapist’s behavior. This algorithm allows learning and imitating repetitive movement tasks. Our experimental results show the application of these algorithms to repetitive motion task.

Keywords

Learning from demonstrationRobotic rehabilitationFourier series
Type
Articles
Information
Robotica , Volume 38 , Issue 2 , February 2020 , pp. 337 - 349
Copyright
© Cambridge University Press 2019 

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