Volume 20, Issue 1 (January 2020)                   Modares Mechanical Engineering 2020, 20(1): 129-137 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Abbasi Moshaei A, Mohammadi Moghaddam M, Dehghan Neistanak V. Analytical Model of Hand Phalanges Desired Trajectory for Rehabilitation and Design a Sliding Mode Controller Based on This Model. Modares Mechanical Engineering 2020; 20 (1) :129-137
URL: http://mme.modares.ac.ir/article-15-29886-en.html
1- Applied Design Department, Mechanical Engineering Faculty, Tarbiat Modares University, Tehran, Iran
2- Applied Design Department, Mechanical Engineering Faculty, Tarbiat Modares University, Tehran, Iran , m.moghadam@modares.ac.ir
Abstract:   (3275 Views)

Modeling the movement of different parts of the body has been studied a lot in recent years. Body movement models such as fingers movements are good guides for designing different robots. Also, motion disability is one of the common diseases that have a great impact on patients' life quality. To treat the rupture of finger tendon, individual rehearsal rehabilitation exercises for each phalanx is required. In order to achieve this aim and take control of each phalanx movement, the mathematical model of the desired trajectory for each joint is necessary. The angle of each joint is measured with the help of a gyro sensor installed on a novel wearable rehabilitation robot proposed in this paper. The mathematical models of the phalanges motions are obtained by curve fitting. The model is applicable not only in the rehabilitation robots but also in the other robotic works. In most of the works in this area, the desired trajectory diagram was drawn and tracking of the trajectory was investigated. Thus, the desired trajectory formula should be fined for the other works. But in this work, the corresponding formula was found and it can help other researchers to easily use of these formulas for their work. To ensure the accuracy and efficiency of the calculated trajectories, the trajectories are implemented in a control system. In order to control this system, a suitable sliding mode controller was designed and the results of system controlling and trajectory tracking using this controller was obtained.

Full-Text [PDF 1057 kb]   (2634 Downloads)    
Article Type: Original Research | Subject: Mechatronics
Received: 2019/02/16 | Accepted: 2019/05/7 | Published: 2020/01/20

1. Jaworski CA, Krause M, Brown J. Rehabilitation of the wrist and hand following sports injury. Clinics in Sports Medicine. 2010;29(1):61-80. [Link] [DOI:10.1016/j.csm.2009.09.007]
2. Hadi A, Alipour K, Kazeminasab S, Elahinia M. ASR glove: A wearable glove for hand assistance and rehabilitation using shape memory alloys. Journal of Intelligent Material Systems and Structures. 2018;29(8):1575-1585. [Link] [DOI:10.1177/1045389X17742729]
3. Kazeminasab S, Hadi A, Alipour Kh, Elahinia M. Force and motion control of a tendon-driven hand exoskeleton actuated by shape memory alloys. Industrial Robot: An International Journal. 2018;45(5):623-633. [Link] [DOI:10.1108/IR-01-2018-0020]
4. Su YY, Yu YL, Lin CH, Lan CC. A compact wrist rehabilitation robot with accurate force/stiffness control and misalignment adaptation. International Journal of Intelligent Robotics and Applications. 2019;3(1):45-58. [Link] [DOI:10.1007/s41315-019-00083-6]
5. Yamamoto I, Matsui M, Higashi T, Iso N, Hachisuka K, Hachisuka A. Wrist rehabilitation robot system and its effectiveness for patients. Sensors and Materials. 2018;30(8):1825-1830. [Link] [DOI:10.18494/SAM.2018.1901]
6. Yap HK, Lim JH, Goh JCH, Yeow CH. Design of a soft robotic glove for hand rehabilitation of stroke patients with clenched fist deformity using inflatable plastic actuators. Journal of Medical Devices. 2016;10(4):044504. [Link] [DOI:10.1115/1.4033035]
7. Stilli A, Cremoni A, Bianchi M, Ridolfi A, Gerii F, Vannetti F, et al. AirExGlove- a novel pneumatic exoskeleton glove for adaptive hand rehabilitation in post-stroke patients. IEEE International Conference on Soft Robotics (RoboSoft), 2018 April 24-28, Livorno, Italy. Piscataway: IEEE; 2018. [Link] [DOI:10.1109/ROBOSOFT.2018.8405388]
8. Santello M, Soechting JF. Gradual molding of the hand to object contours. Journal of Neurophysiology. 1998;79(3):1307-1320. [Link] [DOI:10.1152/jn.1998.79.3.1307]
9. Santello M, Flanders M, Soechting JF. Patterns of hand motion during grasping and the influence of sensory guidance. Journal of Neuroscience. 2002;22(4):1426-1435. [Link] [DOI:10.1523/JNEUROSCI.22-04-01426.2002]
10. Soechting JF, Flanders M. Flexibility and repeatability of finger movements during typing: Analysis of multiple degrees of freedom. Journal of Computational Neuroscience. 1997;4(1):29-46. [Link] [DOI:10.1023/A:1008812426305]
11. Mason CR, Gomez JE, Ebner TJ. Hand synergies during reach-to-grasp. Journal of Neurophysiology. 2001;86(6):2896-2910. [Link] [DOI:10.1152/jn.2001.86.6.2896]
12. Castellini C, Van Der Smagt P. Surface EMG in advanced hand prosthetics. Biological Cybernetics. 2009;100(1):35-47. [Link] [DOI:10.1007/s00422-008-0278-1]
13. Kajitani I, Iwata M, Harada M, Higuchi T. A myoelectric controlled prosthetic hand with an evolvable hardware LSI chip. Technology and Disability. 2003;15(2):129-143. [Link] [DOI:10.3233/TAD-2003-15208]
14. Tenore FVG, Ramos A, Fahmy A, Acharya S, Etienne-Cummings R, Thakor NV. IEEE Transactions on Biomedical Engineering. 2009;65(5):1427-1434. [Link] [DOI:10.1109/TBME.2008.2005485]
15. Lee D, Park Y. Vision-based remote control system by motion detection and open finger counting. IEEE Transactions on Consumer Electronics. 2009;55(4):2308-2313. [Link] [DOI:10.1109/TCE.2009.5373803]
16. Eppner C, Deimel R, Alvarez-Ruiz J, Maertens M, Brock O. Exploitation of environmental constraints in human and robotic grasping. The International Journal of Robotics Research. 2015;34(7):1021-1038. [Link] [DOI:10.1177/0278364914559753]
17. Conti R, Allotta B, Meli E, Ridolfi A. Development, design and validation of an assistive device for hand disabilities based on an innovative mechanism. Robotica. 2017;35(4):892-906. [Link] [DOI:10.1017/S0263574715000879]
18. Hussain I, Spagnoletti G, Salvietti G, Prattichizzo D. Toward wearable supernumerary robotic fingers to compensate missing grasping abilities in hemiparetic upper limb. The International Journal of Robotics Research. 2017;36(13-14):1414-1436. [Link] [DOI:10.1177/0278364917712433]
19. Neha E, Suhaib M, Mukherjee S. Contact points determination and validation for grasping of different objects by a four-finger robotic hand. International Journal of Intelligent Machines and Robotics. 2019;1(3). [Link] [DOI:10.1504/IJIMR.2019.101757]
20. Lei C, Zhang H, Liu Y, Du X. Net-flow fingerprint model based on optimization theory. Arabian Journal for Science and Engineering. 2016;41(8):3081-3088. [Link] [DOI:10.1007/s13369-016-2073-y]
21. Arora R, Bera TK. Trajectory tracking of 3D hybrid manipulator through human hand motion. Arabian Journal for Science and Engineering. 2019;44(2):935-947. [Link] [DOI:10.1007/s13369-018-3323-y]
22. Sarac M, Solazzi M, Otaduy MA, Frisoli A. Rendering strategies for underactuated hand exoskeletons. IEEE Robotics and Automation Letters. 2018;3(3):2087-2092. [Link] [DOI:10.1109/LRA.2018.2809916]
23. Yosefi F, Alipour K, Tarvirdizadeh B, Hadi A. Control of knee rehabilitation robot based on combination of backstepping and admittance algorithms. Modares Mechanical Engineering. 2017;16(12):135-143. [Persian] [Link] [DOI:10.1109/RIOS.2017.7956443]
24. Pehlivan AU, Losey DP, O'Malley MK. Minimal assist-as-needed controller for upper limb robotic rehabilitation. IEEE Transactions on Robotics. 2016;32(1):113-124. [Link] [DOI:10.1109/TRO.2015.2503726]
25. Mohamaddan S, Osman MS. Development of grip mechanism assistant device for finger rehabilitation. Service Robotics and Mechatronics. 2010;95-100. [Link] [DOI:10.1007/978-1-84882-694-6_17]
26. Hussain Sh, Jamwal PK, Ghayesh MH, Xie SQ. Assist-as-needed control of an intrinsically compliant robotic gait training orthosis. IEEE Transactions on Industrial Electronics. 2016;64(2):1675-1685. [Link] [DOI:10.1109/TIE.2016.2580123]
27. Chen SH, Lien WM, Wang WW, Lee GD, Hsu LC, Lee KW, et al. Assistive control system for upper limb rehabilitation robot. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2016;24(11):1199-1209. [Link] [DOI:10.1109/TNSRE.2016.2532478]
28. Khoshdel V, Akbarzadeh Tootoonchi A. Robust impedance control for rehabilitation robot. Modares Mechanical Engineering. 2015;15(8):429-437. [Persian] [Link]
29. Taherifar A, Vossoughi Gh, Selk Ghafari A. Identification and torque control of series elastic actuator of lower limb extremity exoskeleton. Modares Mechanical Engineering. 2017;17(8):1-8. [Persian] [Link]
30. Alexander B, Viktor K. Proportions of hand segments. International Journal of Morphology. 2010;28(3):755-758. [Link] [DOI:10.4067/S0717-95022010000300015]

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.