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Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

Published online by Cambridge University Press:  28 October 2021

Shokoufeh Davarzani Open the ORCID record for Shokoufeh Davarzani [Opens in a new window] ,
Mohammad Ali Ahmadi-Pajouh  and
Hamed Ghafarirad
Shokoufeh Davarzani
Affiliation:
Independent Mechatronics Group, Amirkabir University of Technology, Tehran, Iran
Mohammad Ali Ahmadi-Pajouh*
Affiliation:
Independent Mechatronics Group, Amirkabir University of Technology, Tehran, Iran Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
Hamed Ghafarirad
Affiliation:
Independent Mechatronics Group, Amirkabir University of Technology, Tehran, Iran Mechanical Engineering Department, Amirkabir University of Technology, Tehran, Iran
*
*Corresponding author. E-mail: pajouh@aut.ac.ir
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Abstract

Safe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2 $^\circ$ and 3 $^\circ$ , sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1 $^\circ$ ). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

Keywords

rehabilitationinertial sensorKalman filterexperimental modelingsoft actuator
Type
Research Article
Information
Robotica , Volume 40 , Issue 7 , July 2022 , pp. 2091 - 2111
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Van der Loos, H. M., Reinkensmeyer, D. J. and Guglielmelli, E., “Rehabilitation and Health Care Robotics,” In: Springer Handbook of Robotics (Springer, 2016) pp. 1685–1728.CrossRefGoogle Scholar
Aggogeri, F., Mikolajczyk, T. and O’Kane, J., “Robotics for rehabilitation of hand movement in stroke survivors,” Adv. Mech. Eng. 11(4), 1687814019841921 (2019).CrossRefGoogle Scholar
Banerjee, H., Tse, Z. T. H. and Ren, H., “Soft robotics with compliance and adaptation for biomedical applications and forthcoming challenges,” Int. J. Robot. Autom. 33(1), 68–80 (2018).Google Scholar
Zhang, Y. and Lu, M., “A review of recent advancements in soft and flexible robots for medical applications,” Int. J. Med. Robot. Comput. Assisted Surg. 16(3), e2096 (2020).Google ScholarPubMed
Rashid, A. and Hasan, O., “Wearable technologies for hand joints monitoring for rehabilitation: A survey,” Microelectron. J. 88, 173–183 (2019).CrossRefGoogle Scholar
Carbone, G., Gerding, E. C., Corves, B., Cafolla, D., Russo, M. and Ceccarelli, M., “Design of a two-dofs driving mechanism for a motion-assisted finger exoskeleton,” Appl. Sci. 10(7), 2619 (2020).CrossRefGoogle Scholar
Meng, Q., Xiang, S. and Yu, H., “Soft Robotic Hand Exoskeleton Systems: Review and Challenges Surrounding the Technology,” 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017) (Atlantis Press, 2017).CrossRefGoogle Scholar
Polygerinos, P., Lyne, S., Wang, Z., Nicolini, L. F., Mosadegh, B., Whitesides, G. M. and Walsh, C. J., “Towards a Soft Pneumatic Glove for Hand Rehabilitation,” 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2013) pp. 1512–1517.CrossRefGoogle Scholar
Kim, D. H., Lee, S. W. and Park, H.-S., “Sensor Evaluation for Soft Robotic Hand Rehabilitation Devices,” 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob) (IEEE, 2016) pp. 1220–1223.Google Scholar
Chen, X., Gong, L., Wei, L., Yeh, S.-C., Da Xu, L., Zheng, L. and Zou, Z., “A wearable hand rehabilitation system with soft gloves,” IEEE Trans. Ind. Inf. 17(2), 943–952 (2020).CrossRefGoogle Scholar
Saggio, G., Riillo, F., Sbernini, L. and Quitadamo, L. R., “Resistive flex sensors: A survey,” Smart Mater. Struct. 25(1), 013001 (2015).CrossRefGoogle Scholar
Oliver-Salazar, M., Szwedowicz-Wasik, D., Blanco-Ortega, A., Aguilar-Acevedo, F. and Ruiz-González, R., “Characterization of pneumatic muscles and their use for the position control of a mechatronic finger,” Mechatronics 42, 25–40 (2017).CrossRefGoogle Scholar
Paun, M.-A., Sallese, J.-M. and Kayal, M., “Hall effect sensors design, integration and behavior analysis,” J. Sens. Actuator Networks 2(1), 85–97 (2013).CrossRefGoogle Scholar
Mohamed, A., Ren, J., El-Gindy, M., Lang, H. and Ouda, A., “Literature survey for autonomous vehicles: Sensor fusion, computer vision, system identification and fault tolerance,” Int. J. Autom. Control 12(4), 555–581 (2018).CrossRefGoogle Scholar
Noordin, A., Basri, M. and Mohamed, Z., “Sensor fusion algorithm by complementary filter for attitude estimation of quadrotor with low-cost imu,” Telkomnika 16(2), 868–875 (2018).CrossRefGoogle Scholar
Islam, T., Islam, M. S., Shajid-Ul-Mahmud, M. and Hossam-E-Haider, M., “Comparison of Complementary and Kalman Filter based Data Fusion for Attitude Heading Reference System,” AIP Conference Proceedings, vol. 1919 (AIP Publishing LLC, 2017) p. 020002.CrossRefGoogle Scholar
Lin, B.-S., Lee, I.-J., Chiang, P.-Y., Huang, S.-Y. and Peng, C.-W., “A modular data glove system for finger and hand motion capture based on inertial sensors,” J. Med. Biol. Eng. 39(4), 532–540 (2019).CrossRefGoogle Scholar
Haghshenas-Jaryani, M., Pande, C. and Wijesundara, B. M., “Soft Robotic Bilateral Hand Rehabilitation System for Fine Motor Learning,” 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR) (IEEE, 2019) pp. 337–342.CrossRefGoogle Scholar
Lin, B.-S., Lee, I., Yang, S.-Y., Lo, Y.-C., Lee, J. and Chen, J.-L., “Design of an inertial-sensor-based data glove for hand function evaluation,” Sensors 18(5), 1545 (2018).CrossRefGoogle ScholarPubMed
Fan, B., Li, Q. and Liu, T., “How magnetic disturbance influences the attitude and heading in magnetic and inertial sensor-based orientation estimation,” Sensors 18(1), 76 (2018).CrossRefGoogle Scholar
Hazman, M. A. W., Nordin, I. N. A. M., Noh, F. H. M., Khamis, N., Razif, M., Faudzi, A. A. and Hanif, A. S. M., “Imu sensor-based data glove for finger joint measurement,” Indonesian J. Electr. Eng. Comput. Sci. 20(1), 82–88 (2020).CrossRefGoogle Scholar
Zolfagharian, A., Kaynak, A., Yang Khoo, S., Zhang, J., Nahavandi, S. and Kouzani, A., “Control-oriented modelling of a 3d-printed soft actuator,” Materials 12(1), 71 (2019b).CrossRefGoogle Scholar
Shapiro, Y., Gabor, K. and Wolf, A., “Modeling a hyperflexible planar bending actuator as an inextensible euler–bernoulli beam for use in flexible robots,” Soft Robot. 2(2), 71–79 (2015).CrossRefGoogle Scholar
de Payrebrune, K. M. and O’Reilly, O. M., “On constitutive relations for a rod-based model of a pneu-net bending actuator,” Extreme Mech. Lett. 8, 38–46 (2016).CrossRefGoogle Scholar
Wang, T., Zhang, Y., Chen, Z. and Zhu, S., “Parameter identification and model-based nonlinear robust control of fluidic soft bending actuators,” IEEE/ASME Trans. Mechatron. 24(3), 1346–1355 (2019).CrossRefGoogle Scholar
Zolfagharian, A., Kaynak, A., Noshadi, A. and Kouzani, A. Z., “System identification and robust tracking of a 3d printed soft actuator,” Smart Mater. Struct. 28(7), 075025 (2019a).CrossRefGoogle Scholar
Demenkov, M., “Experimental Investigation of Viscoelastic Hysteresis in a Flex Sensor,” In: Extended Abstracts Spring 2018 (Springer, 2019) pp. 231–235.CrossRefGoogle Scholar
Elgeneidy, K., Lohse, N. and Jackson, M., “Data-driven bending angle prediction of soft pneumatic actuators with embedded flex sensors,” IFAC-PapersOnLine 49(21), 513–520 (2016).CrossRefGoogle Scholar
Elgeneidy, K., Lohse, N. and Jackson, M., “Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors–a data-driven approach,” Mechatronics 50, 234–247 (2018).CrossRefGoogle Scholar
Kayri, M., “Predictive abilities of bayesian regularization and levenberg–marquardt algorithms in artificial neural networks: A comparative empirical study on social data,” Math. Comput. Appl. 21(2), 20 (2016).Google Scholar
Kumar, R., Aggarwal, R. and Sharma, J., “Comparison of regression and artificial neural network models for estimation of global solar radiations,” Renewable Sustainable Energy Rev. 52, 1294–1299 (2015).CrossRefGoogle Scholar
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A. and Arshad, H., “State-of-the-art in artificial neural network applications: A survey,” Heliyon 4(11), e00938 (2018).CrossRefGoogle ScholarPubMed
Chu, C.-Y. and Patterson, R. M., “Soft robotic devices for hand rehabilitation and assistance: A narrative review,” J. Neuroeng. Rehabil. 15(1), 1–14 (2018).CrossRefGoogle ScholarPubMed
Ansari, Y., Manti, M., Falotico, E., Mollard, Y., Cianchetti, M. and Laschi, C., “Towards the development of a soft manipulator as an assistive robot for personal care of elderly people,” Int. J. Adv. Robot. Syst. 14(2), 1729881416687132 (2017).CrossRefGoogle Scholar
Heung, H. L., Tang, Z. Q., Shi, X. Q., Tong, K. Y. and Li, Z., “Soft rehabilitation actuator with integrated post-stroke finger spasticity evaluation,” Front. Bioeng. Biotechnol. 8, 111 (2020).CrossRefGoogle ScholarPubMed