Robotic digital twin as a training platform for rehabilitation health personnel
DOI:
https://doi.org/10.29019/enfoqueute.971Keywords:
Digital twin, Exoskeleton, Matlab/Simulink®, medical rehabilitation platform, simulation, rehabilitation roboticsAbstract
In the last twenty years, a trend towards the digitization of products and processes has been generated, so the new concept of health 4.0 promotes the use of simulators and digital twins for the training of health personnel, as well as for the personalized planning of patients rehabilitation treatments. This paper presents a virtual training tool for health personnel, which is based on the prototype of a digital twin of an exoskeleton for upper limb rehabilitation. The device was designed in Solidworks® and later its equivalent model was obtained in Matlab/Simulink® software. Through the latter, the functionality of the device is evaluated through the implementation of different therapeutic routines, which help the physiotherapist to plan and evaluate the performance of each patient’s treatment. In this case, the evaluation of the movements of the upper limb are generated through independent and combined movements of the shoulder,
elbow and wrist joints and, as a result, graphs of the movements are obtained, , as well as a virtual representation of the digital twin and the values of the torques in each joint. Finally, a digital tool that allows the digital twin prototype to be configured to conditions similar to those of the real exoskeleton for automation and supervision tasks is obtained.
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M. Singh, E. Fuenmayor, E. P. Hinchy, Y. Qiao, N. Murray, and D. Devine, “Digital twin: Origin to future,” Applied System Innovation, vol. 4, no. 2, 2021. [Online]. Available: https://www.mdpi.com/2571-5577/4/2/36
R. Zhang, F. Wang, J. Cai, Y. Wang, H. Guo, and J. Zheng, “Digital twin and its applications: A survey,” The International Journal of Advanced Manufacturing Technology, vol. 123, pp. 4123-4136, 2022. [Online]. Available: https://link.springer.com/article/10.1007/s00170-022-10445-3
R. Wagner, B. Schleich, B. Haefner, A. Kuhnle, S. Wartzack, and G. Lanza, “Challenges and potentials of digital twins and industry 4.0 in product design and production for high performance products,” Procedia CIRP, vol. 84, pp. 88-93, 2019, 29th CIRP Design Conference 2019, 08-10 May 2019, Póvoa de Varzim, Portgal. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2212827119308637
Z. Bi, C. W. Zhang, C. Wu, and L. Li, “New digital triad (dt-ii) concept for lifecycle information integration of sustainable manufacturing systems,” Journal of Industrial Information Integration, vol. 26, p. 100316, 2022. [Online]. Available: https://www.sciencedirect.com/ science/article/pii/S2452414X21001096
J. Sresakoolchai and S. Kaewunruen, “Railway infrastructure maintenance efficiency improvement using deep reinforcement learning integrated with digital twin based on track geometry and component defects,” Scientific Reports, vol. 13, no. 1, p. 2439, 2023. [Online]. Available: https://www.nature.com/articles/s41598-023-29526-8
L. Oliveira, M. Castro, R. Ramos, J. Santos, J. Silva, and L. Dias, “Digital twin for monitoring containerized hazmat cargo in port areas,” in 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), 2022, pp. 1-4. [Online]. Available: https://ieeexplore.ieee.org/document/9820434
W. Yu, P. Patros, B. Young, E. Klinac, and T. G. Walmsley, “Energy digital twin technology for industrial energy management: Classification, challenges and future,” Renewable and Sustainable Energy Reviews, vol. 161, p. 112407, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S136403212200315X
S. Y. Teng, M. Touû, W. D. Leong, B. S. How, H. L. Lam, and V. Másˇa, “Recent advances on industrial data-driven energy savings: Digital twins and infrastructures,” Renewable and Sustainable Energy Reviews, vol. 135, p. 110208, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1364032120304974
Y. Liu, L. Zhang, Y. Yang, L. Zhou, L. Ren, F. Wang, R. Liu, Z. Pang, and M. J. Deen, “A novel cloud-based framework for the elderly healthcare services using digital twin,” IEEE Access, vol. 7, pp. 49 088-49 101, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8686260
H. Elayan, M. Aloqaily, and M. Guizani, “Digital twin for intelligent context-aware iot healthcare systems,” IEEE Internet of Things Journal, vol. 8, no. 23, pp. 16 749-16 757, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9320532
G. White, A. Zink, L. Codecá, and S. Clarke, “A digital twin smart city for citizen feedback,” Cities, vol. 110, p. 103064, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0264275120314128
H. Xia, Z. Liu, M. Efremochkina, X. Liu, and C. Lin, “Study on city digital twin technologies for sustainable smart city design: A review and bibliometric analysis of geographic information system and building information modeling integration,” Sustainable Cities and Society, vol. 84, p. 104009, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2210670722003298
P. Angin, M. H. Anisi, F. Göksel, C. Gürsoy, and A. Büyükgülcü, “Agrilora: a digital twin framework for smart agriculture.” J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl., vol. 11, no. 4, pp. 77–96, 2020. [Online]. Available: extension://elhekieabhbkpmcefcoobjddigjcaadp/http://jowua.com/wp-content/uploads/2022/12/jowua-v11n4-6.pdf
W. Purcell and T. Neubauer, “Digital twins in agriculture: A state-of-the-art review,” Smart Agricultural Technology, vol. 3, p. 100094, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/ pii/S2772375522000594
D. Piromalis and A. Kantaros, “Digital twins in the automotive industry: The road toward physical-digital convergence,” Applied System Innovation, vol. 5, no. 4, 2022. [Online]. Available: https://www.mdpi.com/2571-5577/5/4/65
Y. H. Son, K. T. Park, D. Lee, S. W. Jeon, and S. Do Noh, “Digital twin–based cyber-physical system for automotive body production lines,” The International Journal of Advanced Manufacturing Technology, vol. 115, pp. 291-310, 2021. [Online]. Available: https://link.springer.com/article/10.1007/s00170-021-07183-3
A. Liljaniemi and H. Paavilainen, “Using digital twin technology in engineering education – course concept to explore benefits and barriers,” Open Engineering, vol. 10, no. 1, pp. 377-385, 2020. [Online]. Available: https://doi.org/10.1515/eng-2020-0040
A. Berisha-Gawlowski, C. Caruso, and C. Harteis, The Concept of a Digital Twin and Its Potential for Learning Organizations. Cham: Springer International Publishing, 2021, pp. 95-114. [Online]. Available: https://doi.org/10.1007/978-3-030-55878-9 6
S. M. Sepasgozar, “Digital twin and web-based virtual gaming technologies for online education: A case of construction management and engineering,” Applied Sciences, vol. 10, no. 13, 2020. [Online]. Available: https://www.mdpi.com/2076-3417/10/13/4678
J. Viola and Y. Chen, Smart Control Engineering Enabled by Digital Twin. Cham: Springer International Publishing, 2023, pp. 73–102. [Online]. Available: https://doi.org/10.1007/978-3-031-22140-8 4
C. C. Madni, A. M.and Madni, Digital Twin: Key Enabler and Complement to Model-Based Systems Engineering. Cham: Springer International Publishing, 2020, pp. 1-23. [Online]. Available: https://doi.org/10.1007/978-3-030-27486-3 37-1
T. Kaarlela, H. Arnarson, T. Pitka¨aho, B. Shu, B. Solvang, and S. Pieska¨, “Common educational teleoperation platform for robotics utilizing digital twins,” Machines, vol. 10, no. 7, 2022. [Online]. Available: https://www.mdpi.com/2075-1702/10/7/577
R. He, G. Chen, C. Dong, S. Sun, and X. Shen, “Data-driven digital twin technology for optimized control in process systems,” ISA Transactions, vol. 95, pp. 221–234, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0019057819302332
K. Zhang, T. Qu, D. Zhou, H. Jiang, Y. Lin, P. Li, H. Guo, Y. Liu, C. Li, and G. Q. Huang, “Digital twin-based opti-state control method for a synchronized production operation system,” Robotics and Computer-Integrated Manufacturing, vol. 63, p. 101892, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S073658451930256X
V. Kuts, T. Otto, T. Ta¨hemaa, and Y. Bondarenko, “Digital twin based synchronised control and simulation of the industrial robotic cell using virtual reality,” Journal of Machine Engineering, vol. Vol. 19, No. 1, pp. 128–145, 2019, opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze s´rodkó w MNiSW przeznaczonych na działalnos´c´ upowszechniajaca nauke (2019). [Online]. Available: https://jme.publisherspanel.com/ resources/html/article/details?id=186898&language=en
X. Liu, D. Jiang, B. Tao, G. Jiang, Y. Sun, J. Kong, X. Tong, G. Zhao, and B. Chen, “Genetic algorithm-based trajectory optimization for digital twin robots,” Frontiers in Bioengineering and Biotechnology, vol. 9, p. 1433, 01 2022. [Online]. Available: https://www.ncbi.nlm.nih. gov/pmc/articles/PMC8784515/
“Inteligencia artificial en redes de sensores de gemelos digitales submarinos.” [Online]. Available: https://doi.org/10.1145/3519301
J. Tao, Z. Sun, L. Li, Q. Zhang, M. Zhu, Z. Zhang, G. Yuan, Y. Tian, H. Xuyan, and C. Lee, “Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications,” Nature Communications, vol. 11, p. 5381, 10 2020. [Online]. Available: https://www.nature.com/articles/s41467-020-19059-3
Z. Zhang, F. Wen, Z. Sun, X. Guo, T. He, and C. Lee, “Artificial intelligence-enabled sensing technologies in the 5g/internet of things era: From virtual reality/augmented reality to the digital twin,” Advanced Intelligent Systems, vol. 4, no. 7, p. 2100228, 2022. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/aisy.202100228
U. Asad, M. Khan, A. Khalid, and W. A. Lughmani, “Human-centric digital twins in industry: A comprehensive review of enabling technologies and implementation strategies,” Sensors, vol. 23, no. 8, 2023. [Online]. Available: https://www.mdpi.com/1424-8220/23/8/3938
J. Corral-Acero, F. Margara, M. Marciniak, C. Rodero, F. Loncaric, Y. Feng, A. Gilbert, J. F. Fernandes, H. A. Bukhari, A. Wajdan et al., “The ‘Digital Twin’ to enable the vision of precision cardiology,” European Heart Journal, vol. 41, no. 48, pp. 4556–4564, 03 2020. [Online]. Available: https://doi.org/10.1093/eurheartj/ehaa159
G. Coorey, G. Figtree, D. Fletcher, V. Snelson, S. Vernon, D. Winlaw, S. Grieve, A. McEwan, J. Yang, P. Qian, K. O’Brien, J. Orchard, J. Kim, S. Patel, and J. Redfern, “The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field,” npj Digital Medicine, vol. 5, p. 126, 08 2022. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9418270/
P.-H. Huang, K.-H. Kim, and M. Schermer, “Ethical issues of digital twins for personalized health care service: Preliminary mapping study,” Journal of Medical Internet Research, vol. 24, p. e33081, 1 2022. [Online]. Available: https://www.jmir.org/2022/1/e33081
T. Erol, A. F. Mendi, and D. Doğan, “The digital twin revolution in healthcare,” in 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2020, pp. 1-7. [Online]. Available: https://www.researchgate.net/publication/347023987_The_Digital_Twin_Revolution_in_Healthcare
K. Zhang, X. Chen, F. Liu, H. Tang, J. Wang, and W. Wen, “System framework of robotics in upper limb rehabilitation on poststroke motor recovery,” Behavioural Neurology, vol. 2018, pp. 1-14, 12 2018. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311736/
J. Nielsen, A. Sørensen, T. Christensen, T. Savarimuthu, and T. Kulvicius, “Individualised and adaptive upper limb rehabilitation with industrial robot using dynamic movement primitives,” 2017, workshop on Advances and challenges on the development, testing and assessment of assistive and rehabilitation robots: Experiences from engineering and human science research ; Conference date: 29-05-2017 Through 03-06-2017. [Online]. Available: https://www.researchgate.net/publication/323078882_Individualised_and_adaptive_upper_limb_rehabilitation_with_industrial_robot_using_dynamic_movement_primitives
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