RESEARCH PAPER
Mechatronic Device for Locomotor Training
1
Faculty of Mechanical Engineering, Department of Theoretical and Applied Mechanics, Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland
Submission date: 2015-08-26
Acceptance date: 2016-12-09
Online publication date: 2016-12-28
Publication date: 2016-12-01
Acta Mechanica et Automatica 2016;10(4):310-315
KEYWORDS
ABSTRACT
This paper presents a novel mechatronic device to support a gait reeducation process. The conceptual works were done by the interdisciplinary design team. This collaboration allowed to perform a device that would connect the current findings in the fields of biomechanics and mechatronics. In the first part of the article shown a construction of the device which is based on the structure of an overhead travelling crane. The rest of the article contains the issues related to machine control system. In the prototype, the control of drive system is conducted by means of two RT-DAC4/PCI real time cards connected with a signal conditioning interface. Authors present the developed control algorithms and optimization process of the controller settings values. The summary contains a comparison of some numerical simulation results and experimental data from the sensors mounted on the device. The measurement data were obtained during the gait of a healthy person.
REFERENCES (25)
1.
Akdogan E., Adli M.A. (2011), The design and control of a therapeutic exercise robot for lower limb rehabilitation: Physiotherabot, Mechatronics, 21(3), 509-522.
2.
Bae J., Tomizuka M. (2012), A gait rehabilitation strategy inspired by an iterative learning algorithm, Mechatronics, 22(2), 213-221.
3.
Behrman A.L., Harkema S.J. (2000), Locomotor training after human spinal cord injury: a series of case studies, Physical Therapy, 80(7), 688-700.
4.
Botticello A.L., Rohrbach T., Cobbold N. (2014), Disability and the built environment: aninvestigation of community and neighborhood land uses and participation for physically impaired adults, Annals of Epidemiology, 24(7), 545-550.
5.
Boyd J.E., Little J.J. (2005), Biometric gait recognition, Advanced Studies in Biometrics, Springer, 3161, 19-42.
6.
Campa R., Kelly R., Santibanez V. (2004), Windows-based real-time control of direct-drive mechanisms: platform description and experiments, Mechatronics, 14(9), 1021-1036.
7.
Cao J., Xie S.Q., Das R., Zhu G.L. (2014), Control strategies for effective robot assisted gait rehabilitation: The state of art and future prospects, Medical engineering & Physics, 36(12), 1555-1566.
8.
Cappozzo A., Della Croce U., Leardini A., Chiari L. (2005), Human movement analysis using stereophotogrammetry Part 1: theoretical background, Gait & Posture, 21(2), 186–196.
9.
Duda S., Gembalczyk G., Kciuk S., Gasiorek D. (2014), Mechatronic device to protect against falls during locomotor rehabilitation, Proceedings of the 3rd Joint International Conference on Multibody System Dynamics, Busan, 121-122.
10.
Duda S., Kawlewski K., Gembalczyk G. (2015), Concept of the System for Control over Keeping up the Movement of a Crane, Solid State Phenomena, 220, 339-344.
11.
Duda S., Michnik R., Kciuk S., Jurkojć J., Kawlewski K., Machoczek T. (2011), The conception of a mechatronic device for locomotor training, Aktualne Problemy Biomechaniki, 5, 29-36.
12.
Faust O., Yu W., Acharya U.R. (2015), The role of real-time in biomedical science: A meta-analysis on computational complexity, delay and speedup, Computers in Biology and Medicine, 58, 73-84.
13.
Gembalczyk G., Duda S. (2012), Design and validation of devices for measuring the force and the angle of inclination rope in crane, Modelowanie inżynierskie, 14(45), 32-38. [in Polish].
14.
Hesse S., Werner C. (2009), Connecting research to the needs of patients and clinicians, Brain Research Bulletin, 78, 26-34.
15.
Hidler J., Brennan D., Black I., Nichols D., Brady K. Nef T. (2011), ZeroG: Overground gait and balance training system, Journal of Rehabilitation Research & Development, 48(4), 287-298.
16.
Hidler J.M., Wall A.E. (2005), Alteration in muscle activation patterns during robotic-assisted walking, Clinical Biomechanics, 20, 184-193.
17.
Hussain S., Xie S.Q., Jamwal P.K. (2013), Control of a robotic orthosis for gait rehabilitation, Robotics and Autonomous Systems, 61(9), 911-919.
18.
Kaliński K.J., Buchholz C. (2015), Mechatronic design of strongly nonlinear systems on a basis of three wheeled mobile platform, Mechanical Systems and Signal Processing, 52-53, 700-721.
19.
Lunenburger L., Colombo G., Riener R., Dietz V. (2004), Biofeedback in gait training with the robotic orthosis Lokomat, Engineering in Medicine and Biology Society, 4888-4891.
20.
Mailah M., Jahanabadi H., Zain M.Z.M., Priyandoko G. (2009), Modelling and control of a human-like arm incorporating muscle models, Journal of Mechanical Engineering Science, 223(7), 1569-1577.
21.
Marchal-Crespo L., Reinkensmeyer D.J. (2009), Review of control strategies for robotic movement training after neurologic injury, Journal of neuroengineering and rehabilitation, 6, 20.
22.
Mulroy S.J., Klassen T., Gronley J.K., Eberly V.J., Brown D.A., Sullivan K.J. (2010), Gait parameters associated with responsiveness to treadmill training with body-weight support after stroke: an exploratory study, Physical Therapy, 90(2), 209-223.
23.
Sawers A., Ting L.H. (2014), Perspectives on human-human sensorimotor interactions for the design of rehabilitation robots, Journal of neuroengineering and rehabilitation, 11, 142.
24.
Sherafat S., Salavati M., Takamjani I.E., Akhbari B., Mohammadirad S., Mazaheri m., Negahban H. (2013), Intrasession and intersession reliability of postural control in participants with and without nonspecific low back pain using the Biodex Balance System, Journal of manipulative and physiological therapeutics, 36(2), 111-118.
25.
Walker M.L., Ringleb S.I., Maihafer G.C., Walker R., Crouch J.R., Van Lunen B., Morrison S. (2010), Virtual reality–enhanced partial body weight–supported treadmill training poststroke: feasibility and effectiveness in 6 subjects. Archives of physical medicine and rehabilitation, 91(1), 115-122.
| eISSN: | 2300-5319 |
| ISSN: | 1898-4088 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
