Abstract
Soft rehabilitation devices have been invented and applied for hand function recovery. In this paper, we propose a new Ring-reinforced 3D printed soft robotic hand, which combines hand rehabilitation and joint stiffness evaluation. The elastomer body of Ring-reinforced Soft-Elastic Composite Actuator (R-SECA) is 3D printed directly for fitting different sizes of fingers and the Iterative learning model predictive control (ILMPC) algorithm is used for controlling. Torque compensating layer inside R-SECA enables finger flexion and extension despite finger spasticity. Plastic rings are used to refrain radial expansion and reinforce the actuator. Bending angle and output tip force at different air pressure inputs are explored with four different R-SECA (120 mm, 112 mm, 96 mm, 72 mm length). Four-stroke survivors are recruited to evaluate the effectiveness of the soft robotic hand, and hand function improvement can be observed from the clinical evaluation data and stiffness evaluation outcomes.
Supported by the Guangdong Science and Technology Research Council (Grant No. 2020B1515120064) and the Hong Kong Innovation and Technology Fund (ITS/065/18FP).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Benjamin, E.J., et al.: Heart disease and stroke statistics-2017 update: a report from the American heart association. Circulation 135(10), e146–e603 (2017)
Takahashi, C.D., Der-Yeghiaian, L., Le, V., Motiwala, R.R., Cramer, S.C.: Robot-based hand motor therapy after stroke. Brain 131(2), 425–437 (2008)
Gerloff, C., Corwell, B., Chen, R., Hallett, M., Cohen, L.G.: The role of the human motor cortex in the control of complex and simple finger movement sequences. Brain J. Neurol. 121(9), 1695–1709 (1998)
Bütefisch, C., Hummelsheim, H., Denzler, P., Mauritz, K.-H.: Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. J. Neurol. Sci. 130(1), 59–68 (1995)
Polygerinos, P., Wang, Z., Galloway, K.C., Wood, R.J., Walsh, C.J.: Soft robotic glove for combined assistance and at-home rehabilitation. Robot. Autonom. Syst. 73, 135–143 (2015)
Ho, N.S.K., et al.: An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation. In: 2011 IEEE International Conference On Rehabilitation Robotics, pp. 1–5. IEEE (2011)
Dovat, L., et al.: Handcare: a cable-actuated rehabilitation system to train hand function after stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 16(6), 582–591 (2008)
Feng, N., Shi, Q., Wang, H., Gong, J., Liu, C., Zhiguo, L.: A soft robotic hand: design, analysis, sEMG control, and experiment. Int. J. Adv. Manuf. Technol. 97(1), 319–333 (2018)
Zhou, J., et al.: A soft-robotic approach to anthropomorphic robotic hand dexterity. IEEE Access 7, 101483–101495 (2019)
Yap, H.K., Lim, J.H., Nasrallah, F., Goh, J.C.H., Yeow, C.-H.: Characterisation and evaluation of soft elastomeric actuators for hand assistive and rehabilitation applications. J. Med. Eng. Technol. 40(4), 199–209 (2016)
Heung, K.H.L., Tong, R.K.Y., Lau, A.T.H., Li, Z.: Robotic glove with soft-elastic composite actuators for assisting activities of daily living. Soft Robot. 6(2), 289–304 (2019)
Shi, X.Q., Heung, H.L., Tang, Z.Q., Tong, K.Y., Li, Z.: Verification of finger joint stiffness estimation method with soft robotic actuator. Front. Bioeng. Biotechnol. 8, 1479 (2020)
Heung, H.L., Tang, Z.Q., Shi, X.Q., Tong, K.Y., Li, Z.: Soft rehabilitation actuator with integrated post-stroke finger spasticity evaluation. Front. Bioeng. Biotechnol. 8, 111 (2020)
Peters, M., Mackenzie, K., Bryden, P.: Finger length and distal finger extent patterns in humans. Am. J. Phys. Anthropol. Official Publ. Am. Assoc. Phys. Anthropol. 117(3), 209–217 (2002)
Polygerinos, P., et al.: Modeling of soft fiber-reinforced bending actuators. IEEE Trans. Robot. 31(3), 778–789 (2015)
Yap, H.K., Sebastian, F., Wiedeman, C., Yeow, C.-H.: Design and characterization of low-cost fabric-based flat pneumatic actuators for soft assistive glove application. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 1465–1470. IEEE (2017)
Heung, K.H.L., Tang, Z.Q., Ho, L., Tung, M., Li, Z., Tong, R.K.Y.: Design of a 3d printed soft robotic hand for stroke rehabilitation and daily activities assistance. In: 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 65–70. IEEE (2019)
Esteki, A., Mansour, J.M.: An experimentally based nonlinear viscoelastic model of joint passive moment. J. Biomech. 29(4), 443–450 (1996)
Kuo, P.-H., Deshpande, A.D.: Muscle-tendon units provide limited contributions to the passive stiffness of the index finger metacarpophalangeal joint. J. Biomech. 45(15), 2531–2538 (2012)
Tang, Z.Q., Heung, H.L., Shi, X.Q., Tong, R.K., Li, Z.: Probabilistic model-based learning control of a soft pneumatic glove for hand rehabilitation. IEEE Trans. Biomed. Eng. 69, 1016–1028 (2021)
Tang, Z.Q., Heung, H.L., Tong, K.Y., Li, Z.: A novel iterative learning model predictive control method for soft bending actuators. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 4004–4010. IEEE (2019)
Bullock, I.M., Zheng, J.Z., De La Rosa, S., Guertler, C., Dollar, A.M.: Grasp frequency and usage in daily household and machine shop tasks. IEEE Trans. Haptics. 6(3), 296–308 (2013)
Camilla Biering Lundquist and Thomas Maribo: The Fugl-Meyer assessment of the upper extremity: reliability, responsiveness and validity of the Danish version. Disabil. Rehabil. 39(9), 934–939 (2017)
Hoonhorst, M.H., et al.: How do Fugl-Meyer arm motor scores relate to dexterity according to the action research arm test at 6 months poststroke? Arch. Phys. Med. Rehabil. 96(10), 1845–1849 (2015)
Hsiao, C.-P., Zhao, C., Do, E.Y.-L.: The digital box and block test automating traditional post-stroke rehabilitation assessment. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM workshops), pp. 360–363. IEEE (2013)
Carr, J.H., Shepherd, R.B., Nordholm, L., Lynne, D.: Investigation of a new motor assessment scale for stroke patients. Phys. Ther. 65(2), 175–180 (1985)
Shi, X.Q., Heung, H.L., Tang, Z.Q., Li, Z., Tong, K.Y.: Effects of a soft robotic hand for hand rehabilitation in chronic stroke survivors. J. Stroke Cerebrovasc. Dis. 30(7), 105812 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, C.Q., Shi, X.Q., Li, Z., Tong, K.Y. (2022). 3D Printed Soft Robotic Hand Combining Post-Stroke Rehabilitation and Stiffness Evaluation. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-13835-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13834-8
Online ISBN: 978-3-031-13835-5
eBook Packages: Computer ScienceComputer Science (R0)