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Using the Bioelectric Signals to Control of Wearable Orthosis of the Elbow Joint with Bi-Muscular Pneumatic Servo-Drive

Published online by Cambridge University Press:  09 July 2019

Ryszard Dindorf Open the ORCID record for Ryszard Dindorf [Opens in a new window]  and
Piotr Wos
Ryszard Dindorf*
Affiliation:
Faculty of Mechatronics and Machine Design, Department of Mechatronic Devices, Kielce University of Technology, Kielce, Poland. E-mail: wos@tu.kielce.pl
Piotr Wos
Affiliation:
Faculty of Mechatronics and Machine Design, Department of Mechatronic Devices, Kielce University of Technology, Kielce, Poland. E-mail: wos@tu.kielce.pl
*
*Corresponding author. E-mail: dindorf@tu.kielce.pl
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Summary

This study presents a new design of a wearable orthosis of elbow joint with a bimuscular pneumatic servo-drive (PSD) with control based on the recording of bioelectric signals (BESs). The authors analyzed the impact of the induced brain activity and the muscular tension within the head of the participant on the BESs that can be used to control the PSD of the elbow joint orthosis. To control the elbow joint orthosis, a distributed control system (DCS) was developed, which contains two control layers: a master layer connected to the device for recording the BES and a direct layer contained in a wireless manner with the controller of the PSD. A kinematic-dynamic model of the elbow joint orthosis, patterned after the biological model of human biceps–triceps, was used in the programming of the PSD controller. A biomimetic dynamic model of the pneumatic muscle actuator (PMA) was used, in which the contraction force results from the adopted exponential static model of the pneumatic muscle (PM). The use of direct visual feedback (DVF) makes it possible for the participant to focus on the movement of the orthosis taking into account the motoric functions of the elbow.

Keywords

Bimuscular driving systemPneumatic servo-driveWearable elbow orthosisWireless interface controllerBioelectric signals
Type
Articles
Information
Robotica , Volume 38 , Issue 5 , May 2020 , pp. 804 - 818
Copyright
Copyright © Cambridge University Press 2019

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References

Ripel, T., Krejsa, J., Hrbacek, J. and Cizmar, I., “Active elbow orthosis,” Int. J. Adv. Robot. Syst. 1, 1–10 (2014).Google Scholar
Rahman, M. H., Rahman, M. J., Cristobal, O. L., Saad, M., Kenné, J. P. and Archambault, P. S., “Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements,” Robotica 33, 19–39 (2015).CrossRefGoogle Scholar
Yap, H. Kai, Lim, J. H., Nasralla, F., Goh, J. C. H. and Yeow, R. C. H., “A Soft Exoskeleton for Hand Assistive and Rehabilitation Application using Pneumatic Actuators with Variable Stiffness,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Washington State Convention Center, Seattle, Washington, DC, USA (2015) pp. 4967–4972.Google Scholar
Herr, H., “Exoskeletons and orthoses: Classification, design challenges and future directions,” J. Neuroeng. Rehabil. 6, 1–9 (2009).CrossRefGoogle ScholarPubMed
Cooper, R. A., Ohnabe, H. and Hobson, D. A., “An introduction to rehabilitation engineering,” In: Series in Medical Physics and Biomedical Engineering (CRC Press Taylor & Francis Group, Boca Raton, FL, 2006).Google Scholar
Shahid, M. K., Fletcher, M., Robati, S. and Pemmaraju, G., “The biomechanical forces that act on the elbow joint,” Ecronicon. Orthop. 1, 1–11 (2015).Google Scholar
Kim, K., Hong, K.-J., Kim, N.-G. and Kwon, T.-K., “Assistance of the elbow flexion motion on the active elbow orthosis using muscular stiffness force feedback,” J. Mech. Sci. Technol. 25, 3195–3203 (2011).CrossRefGoogle Scholar
Kiguchi, K., Esaki, R. and Fukuda, T., “Development of a wearable exoskeleton for daily forearm motion assist,” Adv. Robotics. 19, 751–771 (2005).CrossRefGoogle Scholar
Safanos, A., Development of a Wearable Elbow Orthosis, Master Thesis (Tallinn University of Technology, Tallinn, 2017).Google Scholar
Schulte, H. F., “The characteristics of the McKibben artificial muscle,” In: The Application of External Power in Prosthetics and Orthotics (National Academy of Sciences, Washington, DC, 1961): Publ. 874, Appendix H, pp. 94–115.Google Scholar
Sanchez, R. J. Jr., Wolbrecht, E., Smith, R., Liu, J., Rao, S., Cramer, S., Rahman, T., Bobrow, J. E. and Reinkensmeyer, D. J., “A Pneumatic Robot for Re-training Arm Movement after Stroke: Rationale and Mechanical Design,” Proceedings of the IEEE 9th International Conference on Rehabilitation Robotics, Chicago, IL, USA (2005) pp. 500–504.Google Scholar
Jackson, A. E., Culmer, P. R., Levesley, M. C., Makower, S. G., Cozens, J. A. and Bhakta, B. B., “Acceptability of Robot Assisted Active Arm Exercise as Part of Rehabilitation After Stroke,” Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR’2009), Kyoto, Japan (2009) pp. 103–108.Google Scholar
Pavithrana, A., Skariaa, E., Rajana, R. and Jose, J., “Design and fabrication of pneumatic exoskeleton,” Int. Res. J. Eng. Tech. 4, 36–42 (2017).Google Scholar
Gmerek, A., “Mechanical and Hardware Architecture of the Semi-exoskeleton Arm Rehabilitation Robot,” Arch. Mech. Eng. 4, 557–574 (2013).CrossRefGoogle Scholar
Vetrice, G. and Deaconescu, A., “Elbow Joint Rehabilitation Equipment Actuated by Pneumatic Muscles,” Proceedings of the 4th International Conference on Computing and Solutions in Manufacturing Engineering 2016 (CoSME’16), Brasnov, Romania (2016) pp. 1–6.Google Scholar
Liska, O., More, M. and Vasek, V., “Design of Active Feedback for Rehabilitation Device,” Proceedings of the 20th International Conference on Circuits, Systems, Communications and Computers (CSCC’2016), Corfu, Greece (2016) pp. 1–5.Google Scholar
Karnjanaparichat, T. and Pongvuthithum, R., “Adaptive control for a one-link robot arm actuated by pneumatic muscles,” Chiang Mai J. Sci. 35(3), 437–446 (2008).Google Scholar
Balasubramanian, S., Wei, R., Pérez, P., Shepard, B. A., Koeneman, E., Koeneman, J. and He, J., “RUPERT: An Exoskeleton Robot for Assisting Rehabilitation of Arm Functions,” Proceedings of the 2008 Virtual Rehabilitation, Vancouver, BC, Canada (2008), pp. 163–167.Google Scholar
Kousidou, S., Tsagarakis, N. G., Smith, C. and G., D. Caldwell, “Task-orientated Biofeedback System for the Rehabilitation of the Upper Limb,” Proceedings of the IEEE 10th International Conference Rehabilitation Robotics (ICORR’2007), Noordwijk, The Netherlands (2007) pp. 376–384.Google Scholar
Chang, M.-K., “An adaptive self-organizing fuzzy sliding mode controller for a 2-DOF rehabilitation robot actuated by pneumatic muscle actuators,” Control Eng. Pract. 18, 13–22 (2010).CrossRefGoogle Scholar
Liu, H., Zhang, D., Zhao, Y. and Li, Z. H., “Design and Control of An Upper-limb Power-assist Exoskeleton Ddriven by Pneumatic Muscles,” Proceedings of the 9th JFPS International Symposium on Fluid Power, Matsue, Japan (2014).Google Scholar
Canjun, Y., Jiafan, Z., Jie, Z. and Ying, C., “Flexible exoskeleton elbow joint based on pneumatic power,” Patent CN200984250 (2013).Google Scholar
Pujana-Arrese, A., Mendizabal, A., Bastegieta, K., Arenas, J. and Landaluze, J., “Bio-inspired Position/force Control of a 1-DOF Set-up Powered by Pneumatic Muscles,” Proceedings of the 18th IFAC World Congress, Milano, Italy (2011).CrossRefGoogle Scholar
Ueda, J., Ming, D., Krishnamoorthy, V., Shinohara, M. and Ogasawara, T., “Individual muscle control using an exoskeleton robot for muscle function testing,” IEEE Trans. Neural. Syst. Rehabil. Eng. 4, 1–12 (2010)Google Scholar
Oguntosin, V., Harwin, W. S., Kawamura, S., Nasuto, S. J. and Hayashi, Y., “Development of a Wearable Assistive Soft Robotic Device for Elbow Rehabilitation,” Proceedings of the 11th International Conference on Rehabilitation Robotics, Singapore (2015) pp. 747–752.Google Scholar
Fairclough, S. H., Gilleade, K., Nack, L. E. and Mandryk, R. L., “Brain and Body Interfaces: Designing for Meaningful Interaction,” Proceedings of the ACM CHI Conference on Human Factors in Computing Systems, Vancouver, BC, Canada (2011) pp. 65–68.Google Scholar
Gnanayutham, P. and George, J., “Inclusive design for brain body interfaces,” In: Foundations of Augmented Cognition (Springer Verlag, Berlin, Heidelberg, 2011) pp. 102–111.Google Scholar
Shindo, K., Kawashima, K., Ushiba, J., Ota, N., Ito, M., Ota, T., Kimura, A. and Liu, M., “Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: A preliminary case series study,” J. Rehabil. Med. 43, 951–957 (2011).CrossRefGoogle ScholarPubMed
Furukawa, J., Multi-channel Bio-Signal-Based Human Movement Estimation for Assistive Robot Control Ph.D. Thesis (Osaka University, Osaka, 2016).Google Scholar
Kim, J. H., “Brain-machine interface in robot-assisted neurorehabilitation for patients with stroke and upper extremity weakness – The therapeutic turning point,” Brain Neurorehabil. 9, 1–10 (2016)CrossRefGoogle Scholar
Kamatani, D., Fujiwara, T., Uschiba, J. and Shindo, K., “Study for Evaluation Method of Effect of BMI Rehabilitation by Using near Infrared Spectroscopy,” Proceedings of the Neuro 2010, Kobe, Japan (2010) pp. 2–16.Google Scholar
French, J. A., Rose, C. G. and O’Malley, M. K., “System Characterization of MAHI EXO-II: A Robotic Exoskeleton for Upper Extremity Rehabilitation,” Proceedings of the ASME 2014 Dynamic Systems and Control Conference, San Antonio, TX, USA (2014) pp. 1–5.Google Scholar
Daly, J. J. and Huggins, J. E. “Brain-computer interface: Current and emerging rehabilitation applications,” Arch. Phys. Med. Rehabil. 96(3), 1–7 (2015).CrossRefGoogle ScholarPubMed
Daly, J. J., Jane, E. and Huggins, J. E., “Brain-computer interface: Current and emerging rehabilitation applications,” Arch. Phys. Med. Rehabil. 3, 1–7 (2015).CrossRefGoogle Scholar
Dindorf, R., Woś, P. and Pawelec, K., “Study of the Possibility of use of Bioelectric Signals to Wireless Remote Control of the Electro-pneumatic Positioning Systems,” Proceedings of the 23rd International Conference Engineering Mechanics 2017, Svratka, Czech Republic (2017) pp. 270–273.Google Scholar
Mazur, S., Dindorf, R. and Wos, P., “Remote control of the electro-pneumatic servo drive using biosignals,” Tech. Trans. Mech. 1-M, 245–256 (2013)Google Scholar
Hill, A. V., “The Heat of Shortening and the Dynamic Constants of Muscle,” Proceedings of the Royal Society, London, UK (1938) pp. 612–745.Google Scholar
Phillips, C. A., Repperger, D. W., Neidhard-Doll, A. T. and Reynolds, D. B., “Biomimetic model of skeletal muscle isometric contraction: I. an energetic-viscoelastic model for the skeletal muscle isometric force twitch,” Comput. Biol. Med. 34, 307–322 (2004).CrossRefGoogle ScholarPubMed
Hall, K. L., Dynamic Control for a Pneumatic Muscle Actuator to Achieve Isokinetic Muscle Strengthening, Doctor Thesis (Wright State University, 2011).Google Scholar
Reynolds, D. B., Repperger, D. W., Phillips, C. A. and Bandry, G., “Modeling the dynamic characteristics of pneumatic muscle,” Ann. Biomed. Eng. 31, 310–317 (2003).CrossRefGoogle ScholarPubMed
Dindorf, R., “Modelling of artificial arm actuated by pneumatic muscle actuators,” Int. J. Appl. Mech. Eng. 15(3), 667–683 (2010).Google Scholar
Choi, T.-Y., Choi, B.-S., Lee, J.-J., “Manipulator Operation Using Joint Stiffness Adjusting by Pneumatic Muscles,” Proceedings of the 4th International Conference on Human System Interaction, Krakow, Poland (2008) pp. 434–439.Google Scholar
Klute, G. K., Czerniecki, J. M. and Hannaford, N. B., “Artificial muscles: Actuators for biorobotic systems,” Int. J. Robot. Res. 4, 295–309 (2002).CrossRefGoogle Scholar
Smagt, P., Groen, F. and Schulten, K., “Analysis and control of a rubbertuator arm,” Biol. Cybern. 75, 443–450 (1996).Google Scholar
Shin, D., Khatib, O. and Cutkosky, M., “Design Methodologies of the Hybrid Actuation Approach for Human-friendly Robot,” Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 1–6.Google Scholar
Dindorf, R., “Flexible pneumatic actuators,” Monograph M55 (Kielce University of Technology, Kielce, 2014).Google Scholar
Dindorf, R., Takosoglu, J. and Wos, P., “Developments of pneumatic control systems,” Monograph M89 (Kielce University of Technology, Kielce, 2017).Google Scholar
Kumru, H., Albu, S., Pelayo, R., Rothwell, J., Opisso, E., Leon, D., Soler, D. and Tormos, J. M., “Motor cortex plasticity during unilateral finger movement with mirror visual feedback,” Natural Plast. 2016, 1–8 (2016).CrossRefGoogle ScholarPubMed
Manto, M., Rocon, E., Pons, J., Belda, J. M. and Camut, S., “Evaluation of a wearable orthosis and an associated algorithm for tremor suppression,” Physiol. Meas. 28(4), 415–425 (2007).CrossRefGoogle Scholar
Taheri, B., Case, D. and Richer, E., “Active Tremor Estimation and Suppression in Human Elbow Joint,” Proceedings of the ASME 2011 Dynamic Systems and Control Conference, Arlington, VA, USA (2011) pp. 115–120.Google Scholar
Reynolds, B. and Waechter, A., Brain Computer Interfacing Using the Neural Impulse Actuator. A Usability and Statistical Evaluation, Ph.D. Thesis (California Polytechnic State University, Los Angeles, 2009).Google Scholar
Mazur, S., Dindorf, R. and Wos, P., “Wireless Control of the Electro-pneumatic Servo Drive,” Proceedings of the 33rd International Conference Information Systems Architecture and Technology, Szklarska Poreba, Poland, September 16–18 (2012) pp. 231–240.Google Scholar
Pampu, N. C., “Study of effects of the short time fourier transform configuration on EEG spectral estimates,” Acta. Technica. Napocensis. Electron. Telecom. 52(4), 26–29 (2011).Google Scholar
Mazzeo, M., Drowsiness Detection Through Spectral Estimation of Eeg Signals, Master Thesis (Politecnico di Milano, Milano, 2014).Google Scholar