research-article

Enhancing Engagement during Robot-Assisted Rehabilitation Integrated with Motor Imagery Task

Authors:

Linhong JiAuthors Info & Claims

ICIMH 2019: Proceedings of the 2019 International Conference on Intelligent Medicine and Health

Pages 12 - 16

https://doi.org/10.1145/3348416.3348420

Published: 01 July 2019 Publication History

Abstract

Stroke remains the most common cause of motor deficits for adults. Enhancing engagement has become the focus of recent research with the aim of improving the efficiency of robot-assisted rehabilitation. Since motor imagery (MI) has the potential to engage the subject, the objective of this study is to explore the influence of complementing robot-assisted rehabilitation with MI during training exercises. An experiment was designed and conducted in which 10 healthy subjects were recruited to participate in two separate sessions. An acoustic-cue-based experimental paradigm was applied in both sessions. In the first session, each patient was required to imagine moving arm after the cue, then the robot device drove the arm during the MI process; while in the second session, the robotic device drove the user to move without requiring the MI tasks. Each session consisted of 20 trails, in which electroencephalogram (EEG) was recorded to analyze the activated brain regions. Analyses showed that the activation of sensorimotor cortex (SM1) was the strongest during passive movement (PM) integrated with MI than either PM or MI alone. The results indicated that robot-assisted training integrated with MI task can enhance the subject's engagement as shown by a stronger event related desynchronization (ERD), which can lead to a stronger stimulation on SM1. This indication can explain why only passive movement driven by robotic device has a low rehabilitation efficiency during clinical practice. The result can also contribute to the understanding of the mechanism underlying the brain computer interface (BCI) supported rehabilitation therapy, which can improve rehabilitation efficiency by closing the loop between the motor intention and sensorimotor feedback.

References

[1]

Strong, K., Mathers, C., and Bonita, R. 2007. Preventing stroke: saving lives around the world. Lancet Neurology. 6, 2 (Feb. 2007), 182--187.

[2]

Lynch, D., Ferraro, M., Krol, J., Trudell, C.M., Christos, P., and Volpe, B.T. 2005. Continuous passive motion improves shoulder joint integrity following stroke. Clinical rehabilitation. 19, 6 (Sep. 2005), 594--599.

[3]

Warraich, Z., and Kleim, J.A. 2010. Neural Plasticity: The Biological Substrate for Neurorehabilitation. Pm & R. 2, 12 (Dec. 2010), S208-S219.

[4]

Pehlivan, A.U., Sergi, F., and Malley, M.K.O. 2013. Adaptive control of a serial-in-parallel robotic rehabilitation device. In proceedings of the 2013 IEEE 13th International Conference on Rehabilitation Robotics (Seattle, Washington, USA, June 24-26, 2013), 24--26.

[5]

Pérez-Rodríguez, R., Rodríguez, C., Costa, Ú., Cáceres, C., Tormos, J.M., Medina, J., et al. 2014. Anticipatory assistance-as-needed control algorithm for a multijoint upper limb robotic orthosis in physical neurorehabilitation. Expert Systems with Applications. 41, 8(June 2014), 3922--3934.

Digital Library

[6]

Li, C., Rusák, Z., Horváth, I., and Ji, L. 2016. Development of engagement evaluation method and learning mechanism in an engagement enhancing rehabilitation system. Engineering Applications of Artificial Intelligence. 51 (Jan. 2016), 182--190.

Digital Library

[7]

Li, C., Rusák, Z., Horváth, I., Kooijman, A., and Ji, L. 2017. Implementation and Validation of Engagement Monitoring in an Engagement Enhancing Rehabilitation System. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 25, 6 (June 2017), 726--738.

[8]

Zhang, H., Xu, L., Wang, S., Xie, B., Guo, J., Long, Z., et al. 2011. Behavioral improvements and brain functional alterations by motor imagery training. Brain Research. 1407 (July 2016), 38--46.

[9]

Ang, K.K., Guan, C., Chua, K.S.G., Ang, B.T., Kuah, C., Wang, C., et al. 2010. Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. In proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Buenos Aires, Argentina, Aug. 31, 2010-Sept. 4, 2010). 5549--5552.

[10]

Ang, K.K., Guan, C., Chua, K.S., Ang, B.T., Kuah, C., Wang, C., et al. 2009. A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation. In proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Minneapolis, MN, USA, Sep 2, 2009 - Sep 6, 2009). 5981--5984.

[11]

Ang, K.K., Guan, C., Phua, K.S., Wang, C., Zhou, L., Tang, K.Y., et al. 2014. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front Neuroeng. 7,30 (July 2014).

[12]

Ramos-Murguialday, A., Broetz, D., Rea, M., Läer, L., Yilmaz, Ö., Brasil, F.L., et al. 2013. Brain--machine interface in chronic stroke rehabilitation: A controlled study. Annals of Neurology. 74, 1 (March 2013), 100--108.

[13]

Roseboom, W., Linares, D., and Nishida, S.y. 2015. Sensory adaptation for timing perception. Proceedings of the Royal Society B: Biological Sciences. 282, 1805(April, 2015).

[14]

Wang, K., Wang, Z., Guo, Y., He, F., Qi, H., Xu, M., et al. 2017. A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study. Journal of Neuroengineering and Rehabilitation. 14 (Sep. 2017).

[15]

Bertrand, O., Perrin, F., and Pernier, J. 1985. A theoretical justification of the average reference in topographic evoked potential studies. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section. 62, 6 (Nov. 1985), 462--464.

[16]

Mørup, M., Hansen, L.K., Parnas, J., and Arnfred, S.M. 2006. Decomposing the time-frequency representation of EEG using non-negative matrix and multi-way factorization. University of Denmark.

[17]

Ono, T., Tomita, Y., Inose, M., Ota, T., Kimura, A., Liu, M., et al. 2015. Multimodal Sensory Feedback Associated with Motor Attempts Alters BOLD Responses to Paralyzed Hand Movement in Chronic Stroke Patients. Brain Topography. 28, 2 (July 2014), 340--351.

Cited By

Xu XFan XDong JZhang XSong ZLi WPu F(2024)Event-Related EEG Desynchronization Reveals Enhanced Motor Imagery From the Third Person Perspective by Manipulating Sense of Body Ownership With Virtual Reality for Stroke PatientsIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2024.336558732(1055-1067)Online publication date: 2024
https://doi.org/10.1109/TNSRE.2024.3365587
Jia TLi CMo LQian CJi LLiu A(2022)Recognizing the individualized sensorimotor loop of stroke patients during BMI-supported rehabilitation training based on brain functional connectivity analysisJournal of Neuroscience Methods10.1016/j.jneumeth.2022.109658378(109658)Online publication date: Aug-2022
https://doi.org/10.1016/j.jneumeth.2022.109658
Jamil NBelkacem AOuhbi SLakas A(2021)Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature ReviewSensors10.3390/s2114475421:14(4754)Online publication date: 12-Jul-2021
https://doi.org/10.3390/s21144754

Index Terms

Enhancing Engagement during Robot-Assisted Rehabilitation Integrated with Motor Imagery Task
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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ICIMH 2019: Proceedings of the 2019 International Conference on Intelligent Medicine and Health

July 2019

71 pages

ISBN:9781450372862

DOI:10.1145/3348416

Copyright © 2019 ACM.

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Published: 01 July 2019

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ICIMH 2019: 2019 International Conference on Intelligent Medicine and Health

July 1 - 3, 2019

Ningbo, China

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Cited By

Xu XFan XDong JZhang XSong ZLi WPu F(2024)Event-Related EEG Desynchronization Reveals Enhanced Motor Imagery From the Third Person Perspective by Manipulating Sense of Body Ownership With Virtual Reality for Stroke PatientsIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2024.336558732(1055-1067)Online publication date: 2024
https://doi.org/10.1109/TNSRE.2024.3365587
Jia TLi CMo LQian CJi LLiu A(2022)Recognizing the individualized sensorimotor loop of stroke patients during BMI-supported rehabilitation training based on brain functional connectivity analysisJournal of Neuroscience Methods10.1016/j.jneumeth.2022.109658378(109658)Online publication date: Aug-2022
https://doi.org/10.1016/j.jneumeth.2022.109658
Jamil NBelkacem AOuhbi SLakas A(2021)Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature ReviewSensors10.3390/s2114475421:14(4754)Online publication date: 12-Jul-2021
https://doi.org/10.3390/s21144754

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