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Optimal Synthesis of the Stephenson-II Linkage for Finger Exoskeleton Using Swarm-based Optimization Algorithms

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Abstract

Active exoskeletons have been widely investigated to supplement and restore human hand movements, but a significant limitation is that they have a complicated design requiring multi actuators. Single Degree-Of-Freedom (DOF) planar linkage mechanisms could be used with simple control. This research represents the design and optimization of a mechanism proposed for a finger exoskeleton bionic device. One DOF six-bar linkage Stephenson-II is selected, and a motion-generation mechanism synthesis problem is defined. The design is based on the data obtained from the flexion/extension motion of the index finger through 16 precision points and 16 angles for each phalange associated with the fingertip position. After explaining the kinematic analysis of the Stephenson-II, an evaluation of swarm intelligence techniques, including PSO, GWO, and ARO algorithms for solving optimization problems, is presented. ARO algorithm demonstrates the best performance among them. Moreover, the optimized mechanism in this study has a 50% error reduction compared to the one previously designed (Bataller et al. in Mech Mach Theory 105: 31–43, 2016).

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Data Availability

The authors confirm that the data supporting the findings of this study are available within the article. Derived data supporting the findings of this study are available from the corresponding author [M. Bamdad] on request.

References

  1. Garcia-Gonzalez, A., Fuentes-Aguilar, R. Q., Salgado, I., & Chairez, I. (2022). A review on the application of autonomous and intelligent robotic devices in medical rehabilitation. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44, 1–16.

    Google Scholar 

  2. Qassim, H. M., & Wan Hasan, W. Z. (2020). A review on upper limb rehabilitation robots. Applied Sciences, 10, 6976.

    Google Scholar 

  3. Jin, H., Dong, E., Xu, M., & Yang, J. (2020). A smart and hybrid composite finger with biomimetic tapping motion for soft prosthetic hand. Journal of Bionic Engineering, 17, 484–500.

    Google Scholar 

  4. Li, H., Yu, H., Chen, Y., Tang, X., Wang, D., Meng, Q., & Du, Q. (2022). Design of a minimally actuated lower limb exoskeleton with mechanical joint coupling. Journal of Bionic Engineering, 19, 370–389.

    Google Scholar 

  5. Bamdad, M., & Zarshenas, H. (2015). Robotic rehabilitation with the elbow stiffness adjustability. Modares Mechanical Engineering, 14, 151–158.

    Google Scholar 

  6. Sabaapour, M. R., Lee, H., Afzal, M. R., Eizad, A., & Yoon, J. (2019). Development of a novel gait rehabilitation device with hip interaction and a single DOF mechanism. 2019 International Conference on Robotics and Automation (ICRA). IEEE, pp. 1492–1498.

  7. Liu, J., He, Y., Yang, J., Cao, W., & Wu, X. (2022). Design and analysis of a novel 12-DOF self-balancing lower extremity exoskeleton for walking assistance. Mechanism and Machine Theory, 167, 104519.

    Google Scholar 

  8. Kang, B. B., Choi, H., Lee, H., & Cho, K. J. (2019). Exo-glove poly II: A polymer-based soft wearable robot for the hand with a tendon-driven actuation system. Soft Robotics, 6, 214–227.

    Google Scholar 

  9. Chu, C. Y., & Patterson, R. M. (2018). Soft robotic devices for hand rehabilitation and assistance: A narrative review. Journal of Neuroengineering and Rehabilitation, 15, 1–14.

    Google Scholar 

  10. Li, Z., Hou, Z., Mao, Y., Shang, Y., & Kuta, L. (2020). The development of a two-finger dexterous bionic hand with three grasping patterns-NWAFU hand. Journal of Bionic Engineering, 17, 718–731.

    Google Scholar 

  11. Shahid, T., Gouwanda, D., Nurzaman, S. G., & Gopalai, A. A. (2018). Moving toward soft robotics: A decade review of the design of hand exoskeletons. Biomimetics, 3, 17.

    Google Scholar 

  12. Gopura, R. A. R. C., Kiguchi, K., & Bandara, D. S. V. (2011). A brief review on upper extremity robotic exoskeleton systems. In: 2011 6th International Conference on Industrial and Information Systems, IEEE, pp. 346–351.

  13. Ferguson, P. W., Dimapasoc, B., Shen, Y., & Rosen, J. (2018). Design of a hand exoskeleton for use with upper limb exoskeletons. International Symposium on Wearable Robotics (pp. 276–280). Cham: Springer.

    Google Scholar 

  14. Li, G., Cheng, L., & Sun, N. (2022). Design, manipulability analysis and optimization of an index finger exoskeleton for stroke rehabilitation. Mechanism and Machine Theory, 167, 104526.

    Google Scholar 

  15. Sun, N., Li, G., & Cheng, L. (2021). Design and validation of a self-aligning index finger exoskeleton for post-stroke rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 1513–1523.

    Google Scholar 

  16. Li, H., Cheng, L., & Li, Z. (2020). Design and control of an index finger exoskeleton with cable-driven translational joints. In: 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM), IEEE, pp. 540–545.

  17. Carbone, G., Gerding, E. C., Corves, B., Cafolla, D., Russo, M., & Ceccarelli, M. (2020). Design of a Two-DOFs driving mechanism for a motion-assisted finger exoskeleton. Applied Sciences, 10, 2619.

    Google Scholar 

  18. Carbone, G., Ceccarelli, M., Capalbo, C. E., Caroleo, G., & Morales-Cruz, C. (2022). Numerical and experimental performance estimation for a ExoFing-2 DOFs finger exoskeleton. Robotica, 40, 1820–1832.

    Google Scholar 

  19. Birglen L. Type synthesis of linkage-driven self-adaptive fingers, (2009).

  20. Battezzato, A. (2014). Towards an underactuated finger exoskeleton: An optimization process of a two-phalange device based on kinetostatic analysis. Mechanism and Machine Theory, 78, 116–130.

    Google Scholar 

  21. Wang, J., Liu, Z., & Fei, Y. (2019). Design and testing of a soft rehabilitation glove integrating finger and wrist function. Journal of Mechanisms and Robotics, 11, 011015.

    Google Scholar 

  22. Yap, H. K., Kamaldin, N., Lim, J. H., Nasrallah, F. A., Goh, J. C. H., & Yeow, C. H. (2016). A magnetic resonance compatible soft wearable robotic glove for hand rehabilitation and brain imaging. IEEE transactions on neural systems and rehabilitation engineering, 25, 782–793.

    Google Scholar 

  23. Ma, Z., & Ben-Tzvi, P. (2015). Design and optimization of a five-finger haptic glove mechanism. Journal of Mechanisms and Robotics, 7, 8.

    Google Scholar 

  24. Talat, H., Munawar, H., Hussain, H., & Azam, U. (2022). Design, modeling and control of an index finger exoskeleton for rehabilitation. Robotica, 40, 3514–3538.

    Google Scholar 

  25. Tan, G. R., Robson, N. P., & Soh, G. S. (2017). Motion generation of passive slider multiloop wearable hand devices. Journal of Mechanisms and Robotics, 9, 041011.

    Google Scholar 

  26. Shen, Z., Allison, G., & Cui, L. (2018). An integrated type and dimensional synthesis method to design one degree-of-freedom planar linkages with only revolute joints for exoskeletons. Journal of Mechanical Design, 140, 092302.

    Google Scholar 

  27. Gui, S. (2019). Identification design of one-DOF eight-bar based on limb movements. Mechanism and Machine Theory, 142, 103592.

    Google Scholar 

  28. Zhang, Z., Liao, J., Zhao, J., Liu, X., & Li, H. (2020). Design method of one-DOF bio-inspired mechanism based on layered constraint conditions. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42, 454.

    Google Scholar 

  29. Jabbar, M. A., Bakhy, S. H., & Flaieh, E. H. (2020). A new multi-objective algorithm for underactuated robotic finger during grasping and pinching assignments. IOP Coce Series Materials Science and Engineering (Vol. 671, p. 012135). IOP Publishing.

    Google Scholar 

  30. Bataller, A., Cabrera, J. A., Clavijo, M., & Castillo, J. J. (2016). Evolutionary synthesis of mechanisms applied to the design of an exoskeleton for finger rehabilitation. Mechanism and Machine Theory, 105, 31–43.

    Google Scholar 

  31. Chakraborty, D., Rathi, A., Singh, R., Pathak, V. K., Chaudhary, K., & Chaudhary, H. (2021). Design of a Stephenson III six-bar path generating mechanism for index finger rehabilitation device using nature-inspired algorithms. Neural Computing and Applications, 33, 17315–17329.

    Google Scholar 

  32. Jo, I., Park, Y., Lee, J., & Bae, J. (2019). A portable and spring-guided hand exoskeleton for exercising flexion/extension of the fingers. Mechanism and Machine Theory, 135, 176–191.

    Google Scholar 

  33. Lu, S., Yong, Li., Luo, L., Peng, Y., Tong, Li., & Hui, J. (2021). Development of a finger rehabilitation robot based on sliding slot linkage mechanism. Journal of physics: Conference series (Vol. 2010, p. 012180). IOP Publishing.

    Google Scholar 

  34. Hernández-Santos, C., Davizón, Y. A., Said, A. R., Soto, R., Felix-Herrán, L. C., & Vargas-Martínez, A. (2021). Development of a wearable finger exoskeleton for rehabilitation. Applied Sciences, 11, 4145.

    Google Scholar 

  35. Bamdad, M.,  Bahri, M. M. (2019) Kinematics and manipulability analysis of a highly articulated soft robotic manipulator. Robotica, 37(5), 868–882.

    Google Scholar 

  36. Rahmati, A., Varedi-Koulaei, S. M., Ahmadi, M. H., & Ahmadi, H. (2022). Dynamic synthesis of the alpha-type stirling engine based on reducing the output velocity fluctuations using Metaheuristic algorithms. Energy, 238, 121686.

    Google Scholar 

  37. Nadimi-Shahraki, M. H., Zamani, H., & Mirjalili, S. (2022). Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study. Computers in Biology and Medicine, 148, 105858.

    Google Scholar 

  38. Mokhtari, M., Varedi-Koulaei, S. M., Zhu, J., & Hao, G. (2022). Topology optimization of the compliant mechanisms considering curved beam elements using metaheuristic algorithms. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 236(13), 7197–7208.

  39. Rao, S. S. (2019). Engineering optimization: theory and practice. John Wiley & Sons.

    Google Scholar 

  40. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95-International Conference on Neural Networks, 4, 1942–1948.

    Google Scholar 

  41. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Google Scholar 

  42. Faris, H., Aljarah, I., Al-Betar, M. A., & Mirjalili, S. (2018). Grey wolf optimizer: A review of recent variants and applications. Neural Computing and Applications, 30, 413–435.

    Google Scholar 

  43. Nazari, M., Varedi-Koulaei, S. M., & Nazari, M. (2022). Flow characteristics prediction in a flow-focusing microchannel for a desired droplet size using an inverse model: Experimental and numerical study. Microfluidics and Nanofluidics, 26, 1–19.

    Google Scholar 

  44. Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106–119.

    Google Scholar 

  45. Foroutan, K., Varedi-Koulaei, S. M., Duc, N. D., & Ahmadi, H. (2022). Non-linear static and dynamic buckling analysis of laminated composite cylindrical shell embedded in non-linear elastic foundation using the swarm-based metaheuristic algorithms. European Journal of Mechanics-A/Solids, 91, 104420.

    MathSciNet  MATH  Google Scholar 

  46. Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082.

    Google Scholar 

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Corrective Exercise and Rehabilitation Laboratory, Department of Mechanical Engineering, Shahrood University of Technology, Shahrood, 3619995161, Iran

    Seyyed Mojtaba Varedi-Koulaei, Masoud Mohammadi, Mohammad Amin Malek Mohammadi & Mahdi Bamdad

  2. Department of Health Science and Technology, Aalborg University, 9220, Aalborg, Denmark

    Mahdi Bamdad

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  1. Seyyed Mojtaba Varedi-Koulaei
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Varedi-Koulaei, S.M., Mohammadi, M., Mohammadi, M.A.M. et al. Optimal Synthesis of the Stephenson-II Linkage for Finger Exoskeleton Using Swarm-based Optimization Algorithms. J Bionic Eng 20, 1569–1584 (2023). https://doi.org/10.1007/s42235-022-00327-5

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