Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-24T04:32:53.876Z Has data issue: false hasContentIssue false
  • English
  • Français

Degree of muscle-and-tendon tonus effects on kinesthetic illusion in wrist joints toward advanced rehabilitation robotics

Published online by Cambridge University Press:  20 August 2021

Hiraku Komura Open the ORCID record for Hiraku Komura [Opens in a new window] ,
Takumu Kubo ,
Masakazu Honda  and
Masahiro Ohka
Hiraku Komura*
Affiliation:
Graduate School of Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan
Takumu Kubo
Affiliation:
Graduate School of Informatics, Nagoya University, Nagoya, Aichi, Japan
Masakazu Honda
Affiliation:
Industrial Research Institute of Shizuoka Prefecture, Numazu, Shizuoka, Japan
Masahiro Ohka
Affiliation:
Graduate School of Informatics, Nagoya University, Nagoya, Aichi, Japan
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Due to increasing demand for rehabilitation and therapy for cerebrovascular diseases, patients require advanced development of medical rehabilitation robots. In our laboratory, we focus on the formation capability of the substitute neural path caused by brain plasticity using the kinesthetic illusion (KI), which is effective for therapies using robots. In KI, people perceive an illusionary limb movement without an actual movement when a vibration stimulus is applied to a limb’s tendons. In previous research, the optimal frequency that induces the maximum KI has a correlation factor of about 0.5 with the tendon’s natural frequency when a human subject is in a state of laxity. However, we do not know whether the above finding can be applied to actual rehabilitation because muscles and tendons are sometimes in tonus during rehabilitation, a state that varies the natural frequency. In this study, we investigate the correlation between the optimal and natural frequencies of tendon by systematically changing their tension to clarify the effects on the illusion induced by the muscle and the tendon when they are in tonus. We identified a negative correlation between the optimal and natural frequencies when they are in tonus, although a positive correlation appeared when they are in laxity. This result suggests that KI’s optimal frequency should be changed based on the degree of the tendon and muscle tonus. Therefore, our present findings provide a suitable vibration frequency that induces KI due to the degree of the tendon and muscle tonus during robot therapies.

Keywords

robot therapyneurorehabilitation roboticskinesthetic illusionmuscle spindlenatural frequencyproprioceptionoptimal conditionmodal testpsychophysical experiment
Type
Research Article
Information
Robotica , Volume 40 , Issue 4 , April 2022 , pp. 1222 - 1232
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ministry of Health, Labour and Welfare, “Summary of Patient Survey,” (2017) [Online]. Available: https://www.mhlw.go.jp/toukei/saikin/hw/kanja/17/index.html. [Accessed 20 May 2021].Google Scholar
GBD 2016 Stroke Collaborators, “Global, regional, and national burden of stroke, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016,” Lancet Neurol. 48, 439–458 (2019).Google Scholar
Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A. and Leonhardt, S., “A survey on robotic devices for upper limb rehabilitation,” J. NeuroEng. Rehab. 11(3) (2014).10.1186/1743-0003-11-3CrossRefGoogle ScholarPubMed
Hussain, S., Eschweiler, J., Jamwal, P. K., Vn Vliet, P. and Ghayesh, M. H., “State-of-the-art robotic devices for wrist rehabilitation: Design and control aspects,” IEEE Trans. Human-Mach. Syst. 50(5), 361–372 (2014).Google Scholar
Sasaki, D., Noritsugu, T. and Takaiwa, M., “Development of Active Support Splint Driven by Pneumatic Soft Actuator (ASSIST),” Proceedings IEEE International Conference on Robotics and Automation (ICRA), Spain (2005).Google Scholar
Hu, X. L., Tong, K. Y., Song, R., Zheng, X. J., Lui, K. H., Leung, W. W. F., Ng, S. and Au-Yeung, S. S. Y., “Quantitative evaluation of motor functional recovery process in chronic stroke patients during robot-assisted wrist training,” J. Electromyogr. Kinesiol. 19(4), 639–650 (2009).CrossRefGoogle ScholarPubMed
Loureiro, R. C., Belda-Lois, J. M., Lima, E. R., Pons, J. L., Sanchez-Lacuesta, J. J. and Harwin, W. S., “Upper Limb Tremor Suppression in ADL via an Orthosis Incorporating a Controllable Double Viscous Beam Actuator,” Proceedings 9th International Conference on Rehabilitation Robotics ICORR, Chicago (2005).Google Scholar
Song, R., Tong, K. Y., Hu, X. and Zheng, X. J., “Myoelectrically Controlled Robotic System That Provide Voluntary Mechanical Help for Persons after Stroke,” Proceedings IEEE 10th International Conference on Rehabilitation Robotics (ICORR), Netherlands (2007).CrossRefGoogle Scholar
Rose, C. G., Pezent, E., Kann, C. K., Deshpande, A. D., and O’Malley, M. K., “Assessing wrist movement with robotic devices,” IEEE Trans. Neural Syst. Rehab. Eng. 26(8), 1585–1595 (2018).CrossRefGoogle ScholarPubMed
Khor, K. X.. Chin, P. J. H., Yeong, C. F., Su, E. L. M., Narayanan, A. L. T., Rahman, H. A. and Khan, Q. I.,“Portable and reconfigurable wrist robot improves hand function for post-stroke subjects,” IEEE Trans. Syst, Neural . Rehab. Eng. 25(10), 1864–1873 (2017).CrossRefGoogle Scholar
Pehlivan, A., Sergi, F., Erwin, A., Yozbatiran, N., Francisco, G. and O’Malley, M., “Design and validation of the RiceWrist-S exoskeleton for robotic rehabilitation after incomplete spinal cord injury,” Robotica 32(8), 1415–1431 (2014).CrossRefGoogle Scholar
Martinez, C. and Tavakoli, M., “Learning and reproduction of Therapist’s semi-periodic motions during robotic rehabilitation,” Robotica 38(2), 337–349 (2020).CrossRefGoogle Scholar
Motus, Nova, “Motus Hand,” [Online]. https://motusnova.com/hand/. [Accessed 20 May 2021].Google Scholar
CyberGrasp systems, “CyberGrasp,” [Online]. http://www.cyberglovesystems.com/cybergrasp. [Accessed 20 May 2021].Google Scholar
Adamovich, S., Fluet, G. and Mathai, A., “Design of a complex virtual reality simulation to train finger motion for persons with hemiparesis: a proof of concept study,” J. NeuroEng. Rehab. 6(28), (2009).CrossRefGoogle ScholarPubMed
Neofect, “Neofect Smart Glove,” [Online]. https://www.neofect.com/us/smart-glove. [Accessed 20 May 2021].Google Scholar
Shin, J. H., Kim, M. Y., Lee, J. Y., Jeon, Y. J., Kim, S. and Lee, S., “Effects of virtual reality-based rehabilitation on distal upper extremity function and health-related quality of life: A single-blinded, randomized controlled trial,” J. NeuroEng. Rehab. 13(17) (2016).CrossRefGoogle ScholarPubMed
Naito, E., Ehrsson, H. H., Geyer, S., Zilles, K. and Roland, P. E., “Illusory arm movements activate cortical motor areas: A positron emission tomography study,” J. Neurosci. 19(14), 6134–6144 (1999).CrossRefGoogle ScholarPubMed
Imai, R., Hayashida, K., Nakano, H. and Morioka, S., “Brain activity associated with the illusion of motion evoked by different vibration stimulation devices: An fNIRS study,” J. Phys. Therapy Sci. 26(7), 1115–1119 (2014).CrossRefGoogle Scholar
Imai, R., Osamu, M. and Morioka, S., “Influence of illusory Kinesthesia by vibratory tendon stimulation on acute pain after surgery for distal radius fractures: A Quasi-randomized controlled study,” Clin. Rehab. 30(6), 594–603 (2016).CrossRefGoogle ScholarPubMed
Honda, K. and Kiguchi, K., “Control of human elbow-joint-extension-motion change based on vibration stimulation for upper-limb perception-assist,” IEEE Access 8, 22697–22708 (2020).CrossRefGoogle Scholar
Marasco, P. D., Hebert, J. S., Sensinger, J. W., Shell, C. E., Schofield, J. S., Thumser, Z. C., Nataraj, R., Beckler, D. T., Dawson, M. R., Blustein, D. H., Gill, S., Mensh, B. D., Granja-Vazquez, R., Newcomb, M. D., Carey, J. P. and Orzell, B. M., “Illusory movement perception improves motor control for prosthetic hands,” Sci. Transl. Med. 10(432) (2018).10.1126/scitranslmed.aao6990CrossRefGoogle ScholarPubMed
Roll, J. P. and Vedel, J. P., “Kinaesthetic role of muscle afferents in man, studied by tendon vibration and microneurography,” Exp. Brain Res. 47, 177–190 (1982).CrossRefGoogle ScholarPubMed
Albert, F., Bergenheim, M., Ribot-Ciscar, E. and Roll, J. P., “The Ia afferent feedback of a given movement evokes the illusion of the same movement when returned to the subject via muscle tendon vibration,” Exp. Brain Res. 172(2), 163–174 (2006).CrossRefGoogle Scholar
Roll, J. P., Albert, F. and Thyrion, C., “Inducing any virtual two-dimensional movement in humans by applying muscle tendon vibration,” J. Neurophysiol. 101, 816–823 (2009).CrossRefGoogle ScholarPubMed
Thyrion, C. and Roll, J. P., “Predicting any arm movement feedback to induce three-dimensional illusory movements in humans,” J. Neurophysiol. 104(2), 949–959 (2010).10.1152/jn.00025.2010CrossRefGoogle ScholarPubMed
Honda, M., Karakawa, H., Akahori, K., Miyaoka, T. and Ohka, M., “Estimation of Vibration Stimulus Threshold for Inducing Kinesthetic Illusion,” Proceedings 2014 International Symposium on Micro-NanoMechatronics and Human Science (MHS), pp. 1–2 (2014).10.1109/MHS.2014.7006082CrossRefGoogle Scholar
Kato, Y., Akahori, K., Yoshida, S., Honda, M. and Ohka, M., “A Study of Optimum Stimulus Condition for Inducing Kinesthetic Illusion,” Proceedings 2015 International Symposium on Micro-NanoMechatronics and Human Science (MHS), pp. 1–3 (2015).CrossRefGoogle Scholar
Komura, H., Ikeda, K., Honda, M. and Ohka, M., “Control Method of Kinesthetic Illusion Using Natural Frequency of Tendon Toward Compact Rehabilitation Devices,” Proceedings 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Hong Kong, (2019).10.1109/AIM.2019.8868657CrossRefGoogle Scholar
Thyrion, C. and Roll, J. P., “Perceptual Integration of Illusory and Imagined Kinesthetic Images,” J. Neurosci. 29(26), 8483–8492 (2009).CrossRefGoogle ScholarPubMed
Mukaka, M. M., “A guide to appropriate use of Correlation coefficient in medical research,” Malawi Med. J. 24(3), 69–71 (2012).Google ScholarPubMed
Loren, G. J. and Lieber, R. L., “Tendon biomechanical properties enhance human wrist muscle specialization,” J. Biomech. 28(7), 791–799 (1995).CrossRefGoogle ScholarPubMed
Yamada, H. and Evan, G. F., “Strength of Biological Materials,” The Williams and Wilkins Company, (1970).Google Scholar
Kodama, T., Nakano, H., Ohsugi, H. and Murata, S., “Effects of vibratory stimulation-induced kinesthetic illusions on the neural activities of patients with stroke,” J. Phys. Therapy Sci. 28(2), 419–425 (2016).CrossRefGoogle ScholarPubMed