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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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References

World Heart Federation, “Stroke - World Heart Federation,” http://www.world-heartfederation.org/cardiovascular-health/stroke/ (2017).Google Scholar
Heart and Stroke Foundation, “Statistics - Heart and Stroke Foundation of Canada,” http://www.heartandstroke.com/site/c.ikIQLcMWJtE/b.3483991/k.34A8/Statistics.htm (2017).Google Scholar
Stroke Association, “Explaining a stroke,” http://www.stroke.org/sites/default/files/resources/ExplainingStrokeBrochure.pdf (2017).Google Scholar
Veerbeek, J. M., Kwakkel, G., vanWegen, E. E., Ket, J. C. and Heymans, M.W., “Early prediction of outcome of activities of daily living after stroke: a systematic review,” Stroke 42(5), 1482–1488 (2011).CrossRefGoogle Scholar
Lum, P., Reinkensmeyer, D., Mahoney, R., Rymer, W. Z. and Burgar, C., “Robotic devices for movement therapy after stroke: current status and challenges to clinical acceptance,” Topics in Stroke Rehabilitation 8(4), 40–53 (2002).CrossRefGoogle Scholar
Hogan, N., Krebs, H. I., Charnnarong, J., Srikrishna, P. and Sharon, A., “MIT-MANUS: A Workstation for Manual Therapy and Training. I,” Proceedings IEEE International Workshop on Robot and Human Communication (1992) pp. 161–165.Google Scholar
Maaref, M., Rezazadeh, A., Shamaei, K., Ocampo, R. and Mahdi, T., “A bicycle cranking model for assist-asneeded robotic rehabilitation therapy using learning from demonstration,” IEEE Robot. Autom. Lett., 1(2), 653–660 (2016).CrossRefGoogle Scholar
Sharifi, M., Behzadipour, S., Salarieh, H. and Tavakoli, M., “Cooperative modalities in robotic telerehabilitation using nonlinear bilateral impedance control,” IEEE Robot. Autom. Lett., 67, 52–63 (2017).Google Scholar
Atashzar, F., Polushin, I. G., and Patel, R. V., “Networked Teleoperation with Non-Passive Environment: Application to Telerehabilitation,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Portugal (2012) pp. 5125–5130.CrossRefGoogle Scholar
Atashzar, S. F., Shahbazi, M., Tavakoli, M. and Patel, R. V., “A computational-model-based study of supervised haptics-enabled therapist-in-the-loop training for upper-limb poststroke robotic rehabilitation,” IEEE/ASME Trans. Mech. , 23(2), 563–574 (2018).CrossRefGoogle Scholar
Legg, L., Drummond, A. and Langhorne, P., “Occupational therapy for patients with problems in activities of daily living after stroke,” Cochrane Database of Systematic Reviews, 4 (2006).CrossRefGoogle Scholar
Fong, J. and Tavakoli, M., “Kinesthetic Teaching of a Therapist’s Behavior to a Rehabilitation Robot,” International Symposium on Medical Robotics (ISMR), Atlanta, GA (2018) pp. 1–6.Google Scholar
Lee, H., Suh, I. H., Calinon, S. and Johansson, R., “Learning Basis Skills by Autonomous Segmentation of Humanoid Motion Trajectories,” IEEE-RAS International Conference on Humanoid Robots, Osaka, Japan (2012) pp. 112–119.Google Scholar
Guidali, M., Duschau-Wicke, A., Broggi, S., Klamroth-Marganska, V., Nef, T. and Riener, R., “A robotic system to train activities of daily living in a virtual environment,” Med. Biol. Eng. Comput., 49(10), 1213 (2011).CrossRefGoogle Scholar
Calinon, S., Guenter, F. and Billard, A., “On Learning the Statistical Representation of a Task and Generalizing it to Various Contexts,” IEEE International Conference on Robotics and Automation, Orlando, FL (2006) pp. 2978–2983.Google Scholar
Gribovskaya, E. and Billard, A., “Learning Nonlinear Multi-Variate Motion Dynamics for Real-Time Position and Orientation Control of Robotic Manipulators,” 2009 9th IEEE-RAS International Conference on Humanoid Robots, Paris, France (2009) pp. 472–477.CrossRefGoogle Scholar
Konidaris, G., Kuindersma, S., Grupen, R. and Barto, A., “Robot learning from demonstration by constructing skill trees,” Internat. J. Robot. Res., 31(3), 360–375 (2012).CrossRefGoogle Scholar
Tao, R., “Haptic Teleoperation Based Rehabilitation Systems for Task-Oriented Therapy”, M. S. Thesis (Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada, 2014).Google Scholar
Martinez, C., Fong, J. and Tavakoli, M., “Learning and imitation of a therapists interaction with a patient in robot-assisted cooperative therapy exercises,” Unpublished (2018).Google Scholar
Kido, K., Digital Fourier Analysis: Fundamentals (Springer-Verlag, New York, Incorporated, 2014).Google Scholar
Calinon, S. and Billard, A., “Incremental Learning of Gestures by Imitation in a Humanoid Robot,” ACM/IEEE International Conference on Human-Robot Interaction, Washington, DC (2007) pp. 255–262.Google Scholar
Calinon, S., “A tutorial on task-parameterized movement learning and retrieval,” Intell. Serv. Robot., 9(1), 1–29 (2016).CrossRefGoogle Scholar
Calinon, S., Li, Z., Alizadeh, T., Tsagarakis, N. G. and Caldwell, D. G., “Statistical Dynamical Systems for Skills Acquisition in Humanoids,” IEEE-RAS International Conference on Humanoid Robots, Osaka, Japan (2012) pp. 323–329.Google Scholar
Calinon, S., Robot Programming by Demonstration (EPFL Press, Lausanne, Switzerland, 2009).Google Scholar
Lu, E. C., Wang, R., Huq, R., Gardner, D., Karam, P., Zabjek, K., Hébert, D., Boger, J. and Mihailidis, A., “Development of a robotic device for upper limb stroke rehabilitation: A user-centered design approach,” Paladyn 2(4), 176–184 (2011).Google Scholar