# MODELLING A 1-DOF FINGER EXTENSOR MACHINE FOR HAND REHABILITATION

## Keywords:

Modelling, Simulation, Experimental Validation, Hand Rehabilitation, Hardware-in-the-Loop

## Abstract

It is essential to have an accurate representation of a robotic rehabilitation device in the form of a system model in order to design a robust controller for it. This paper presents mathematical modelling and validation through simulation and experimentation of the 1-DOF Finger Extensor rehabilitation machine. The machine’s design is based on an iris mechanism, built specifically for training open and close movements of the hand. The goal of this research is to provide an accurate model for the Finger Extensor by taking into consideration various factors affecting its dynamics and to present an experimental validation of the devised model. Dynamic system modelling of the machine is performed using Lagrangian formulation and the involved physical parameters are obtained experimentally. To validate the developed model and demonstrate its effectiveness, hardware-in-the-loop experiments are conducted in the Simulink-MATLAB environment. Mean absolute error between the simulated and experimental response is 1.38° and the relative error is 1.13%. The results obtained are found to be within the human motion resolution limits of 5 mm or 5º and exhibit suitability of the model for application in robotic rehabilitation systems. The model accurately replicates the actual behavior of the machine and is suitable for use in controller design.

ABSTRAK: Gambaran tepat mengenai model sistem peranti rehabilitasi robotik adalah sangat penting bagi pembangunan sesebuah reka bentuk alat kawalan tahan lasak. Kajian mengenai model matematik dan pengesahan melalui simulasi dan eksperimentasi mesin pemulihan 1-DOF ‘Finger Extensor’. Mesin ini direka bentuk berdasarkan mekanisme iris, dibangunkan khusus bagi melatih gerakan buka dan tutup tangan. Tujuan kajian ini adalah bagi menyediakan model Finger Extensor yang tepat dengan mengambil kira faktor mempengaruhi dinamik dan pengesahan model eksperimen yang dirancang. Model sistem dinamik mesin ini diuji menggunakan formula Lagrangian dan parameter fizikal yang terlibat diperoleh melalui eksperimen. Model ini disahkan dan diuji keberkesanannya menggunakan eksperimen Perkakasan-dalam-gelung melalui MATLAB-Simulink. Purata ralat mutlak antara dapatan simulasi dan respon eksperimen adalah 1.38° dan ralat relatif 1.13%. Dapatan kajian adalah dalam had resolusi gerakan tangan manusia iaitu 5 mm atau 5º dan didapati model ini sesuai bagi aplikasi sistem rehabilitasi robotik. Model ini tepat dalam mereplikasi kelakuan sebenar mesin dan sesuai digunakan bagi reka bentuk kawalan.

## References

Khor KX, Chin PJH, Rahman HA, Yeong CF, Su ELM, Narayanan ALT. (2014) A novel haptic interface and control algorithm for robotic rehabilitation of stoke patients. In 2014 IEEE Haptics Symposium (HAPTICS), Houston, Texas-USA; pp. 421-426. DOI: https://doi.org/10.1109/HAPTICS.2014.6775492

Plantin J, Pennati GV, Roca P, Baron JC, Laurencikas E, Weber K, Godbolt AK, Borg J, Lindberg PG. (2019) Quantitative assessment of hand spasticity after stroke: imaging correlates and impact on motor recovery. Frontiers in neurology, 10:1-11. DOI: https://doi.org/10.3389/fneur.2019.00836

Cramer SC. (2019) Intense rehabilitation therapy produces very large gains in chronic stroke. Journal of Neurology, Neurosurgery & Psychiatry, 90: 497. DOI: https://doi.org/10.1136/jnnp-2019-320441

Lambercy O, Ranzani R, Gassert R. (2018) Robot-assisted rehabilitation of hand function. In Rehabilitation Robotics. Volume 1. 1st edition. Academic Press; pp 205–225. DOI: https://doi.org/10.1016/B978-0-12-811995-2.00027-8

Popovic MB. (2019) Biomechatronics. Academic Press.

Miao Q, Zhang M, Cao J, Xie SQ. (2018) Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation. Advanced Robotics, 32(24): 1253-1268. DOI: https://doi.org/10.1080/01691864.2018.1546617

Marini F, Hughes CM, Squeri V, Doglio L, Moretti P, Morasso P, Masia L. (2017) Robotic wrist training after stroke: Adaptive modulation of assistance in pediatric rehabilitation. Robotics and Autonomous Systems, 91: 169-178. DOI: https://doi.org/10.1016/j.robot.2017.01.006

Su YY, Yu YL, Lin CH, Lan CC. (2019) A compact wrist rehabilitation robot with accurate force/stiffness control and misalignment adaptation. International Journal of Intelligent Robotics and Applications, 3(1): 45-58. DOI: https://doi.org/10.1007/s41315-019-00083-6

Luo L, Peng L, Wang C, Hou ZG. (2019) A greedy assist-as-needed controller for upper limb rehabilitation. IEEE Transactions on Neural Networks and Learning Systems, 30(11): 3433-3443 DOI: https://doi.org/10.1109/TNNLS.2019.2892157

Pehlivan AU, Losey DP, O’Malley MK. (2015) Minimal assist-as needed controller for upper limb robotic rehabilitation. IEEE Transactions on Robotics; 32(1): 113-124. DOI: https://doi.org/10.1109/TRO.2015.2503726

Pehlivan AU, Losey DP, Rose CG, O'Malley MK. (2017) Maintaining subject engagement during robotic rehabilitation with a minimal assist-as-needed (mAAN) controller. In 2017 International Conference on Rehabilitation Robotics (ICORR): 2017; London; pp. 62–67. DOI: https://doi.org/10.1109/ICORR.2017.8009222

Fricke SS, Bayon C, Rocon E, van der Kooij H, van Asseldonk EH. (2018) Pilot study of a performance based adaptive assistance controller for stroke survivors. In International Conference on Neuro Rehabilitation. Springer, pp. 302–306. DOI: https://doi.org/10.1007/978-3-030-01845-0_61

Stroppa F, Loconsole C, Marcheschi S, Mastronicola N, Frisoli A. (2018) An improved adaptive robotic assistance methodology for upper-limb rehabilitation. In International Conference on Human Haptic Sensing and Touch Enabled Computer Applications. Springer; pp. 513–525. DOI: https://doi.org/10.1007/978-3-319-93399-3_44

Bringmann E, Krämer A. (2008). Model-based testing of automotive systems. in 1st international conference on software testing, verification, and validation. IEEE; pp. 485-493. DOI: https://doi.org/10.1109/ICST.2008.45

Soltani A, Assadian F. (2016) A hardware-in-the-loop facility for integrated vehicle dynamics control system design and validation. IFAC-Papers Online, 49(21): 32-38. DOI: https://doi.org/10.1016/j.ifacol.2016.10.507

Mancisidor A, Zubizarreta A, Cabanes I, Bengoa P, Brull A, Jung JH. (2019) Inclusive and seamless control framework for safe robot-mediated therapy for upper limbs rehabilitation. Mechatronics, 58: 70-79. DOI: https://doi.org/10.1016/j.mechatronics.2019.02.002

Guang H, Ji L, Shi Y, Misgeld BJ. (2018) Dynamic modeling and interactive performance of PARM: A parallel upper-limb rehabilitation robot using impedance control for patients after stroke. Journal of Healthcare Engineering, 2018: 1-11. DOI: https://doi.org/10.1155/2018/8647591

Franco W, Maffiodo D, De Benedictis C, Ferraresi C. (2018) Dynamic modeling and experimental validation of a haptic finger based on a mckibben muscle. in IFToMM Symposium on Mechanism Design for Robotics. Springer; pp. 251–259. DOI: https://doi.org/10.1007/978-3-030-00365-4_30

Ali MAA, Azlan NZ. (2006) Design of iris mechanism for flexion and extension training in hand rehabilitation. ARPN Journal of Engineering and Applied Sciences; 11: 4115-4122.

Osman JHS. (1992) Integrated Model of Industrial Robot for Control Applications. Jurnal Teknologi, 19(1): 27-41. DOI: https://doi.org/10.11113/jt.v19.1055

Germanotta M, Vasco G, Petrarca M, Rossi S, Carniel S, Bertini E, Cappa P, Castelli E. (2015) Robotic and clinical evaluation of upper limb motor performance in patients with Friedreich’s ataxia: an observational study. Journal of Neuroengineering and Rehabilitation, 12(1): 1-3. DOI: https://doi.org/10.1186/s12984-015-0032-6

2021-07-04

## How to Cite

Shahdad, I., Azlan, N. Z., & Jazlan, A. . (2021). MODELLING A 1-DOF FINGER EXTENSOR MACHINE FOR HAND REHABILITATION. IIUM Engineering Journal, 22(2), 384–396. https://doi.org/10.31436/iiumej.v22i2.1706

## Section

Mechatronics and Automation Engineering