Application of Industry 4.0 and digital twin in the field of smart healthcare

Madhab Chandra  Jena; Sarat Kumar  Mishra; Himanshu Sekhar  Moharana

doi:10.18282/iac.v7i1.606

Madhab Chandra Jena Mechanical Engineering, Biju Pattnaik University of Technology, Rourkela, Ishanpur, Jajpur, Odisha 769004, India
Sarat Kumar Mishra Balasore College of Engineeing and Technology, Balasore, Odisha 756060, India
Himanshu Sekhar Moharana Hy-Tech Institute of Technology, Bhubaneswar, Odisha,751001, India

Ariticle ID: 606

64 Views, 37 PDF Downloads

DOI: https://doi.org/10.18282/iac.v7i1.606

Keywords: Industry 4.0; digital twin; IOT; precision medicine; healthcare; big data

Abstract

There is an increasing need for preventive health care as well as precise diagnosis and tailored treatment of different diseases in recent years. Providing customized treatment for each patient and maximizing accuracy and efficiency are main goals of a good healthcare system. This thesis explores the integration of digital twin technology with Industry 4.0 in healthcare. Digital twins create virtual representations of physical systems, enabling real-time monitoring and tailored treatments. Key elements include the living human body, IoT, digital twin, cloud computing, and simulation. The architecture comprises data acquisition, data munging, data storage, data simulation with analytics, and user access layers. Creating a digital twin involves precise 3D modeling, representing the entire body or specific organs. A case study demonstrates real-time monitoring using wearable sensors for blood pressure, blood sugar, and heart rate. Data is transmitted, integrated with the digital twin, and accessible via a website with alarms for abnormal readings. While Industry 4.0 and digital twin adoption in healthcare is evolving, the architecture serves as a reference. It offers real-time data for diagnosis and treatment, with potential for advanced simulations. Challenges include automated data systems and privacy concerns. Despite limitations, this integration holds promise for precision medicine and personalized healthcare.

References

Tao F, Qi Q, Wang L, Nee AYC. Digital twins and cyber-physical systems toward smart manufacturing and Industry 4.0: correlation and comparison. Engineering. 2019; 5(4): 653-662. doi: 10.1016/j.eng.2019.03.009

Glaessgen E, Stargel D. The digital twin paradigm for future NASA and U.S. air force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference; 2012 Apr 23-26; Honolulu, HI, USA. Reston (VA): American Institute of Aeronautics and Astronautics; 2012. doi: 10.2514/6.2012-1824

Tao F, Zhang M, Hu J, et al. Digital twin driven prognostics and health management for complex equipment. CIRP Ann Manuf Technol. 2018; 67(1): 169-172. doi: 10.1016/j.cirp.2018.04.156

Cimino C. Creating value from industrial IoT and big data analytics: lessons learned from industrial SMEs. Technol Forecast Soc Change. 2021; 171: 120980. doi: 10.1016/j.techfore.2021.120980

Qi Q, Tao F. Digital twin and big data toward smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access. 2018; 6: 3585-3593. doi: 10.1109/ACCESS.2017.2770275

Burgun A, Bodenreider O. Accessing and integrating data and knowledge for biomedical research. Yearb Med Inform. 2019; 28(1): 106-112. doi: 10.1055/s-0039-1677906

Tao F, Qi Q. Digital twin technology and its prospect in cloud manufacturing. J Manuf Syst. 2019; 53: 261-266. doi: 10.1016/j.jmsy.2019.03.003

ITU. Internet of Things Global Standards Initiative. Available online: https://www.itu.int/en/ITU-T/gsi/iot/Pages/default.aspx (accessed on 26 June 2015).

Silver Spring M. Using digital twins in healthcare. Available online: https://www.silverspringnet.com/digital-twins-in-healthcare/ (accessed on 26 June 2015).

Jhawat V, Shah D, Mitra P, et al. Review on artificial intelligence techniques in precision agriculture. J Ind Integr Manag. 2020; 5(3): 377-388. doi: 10.1016/j.jiim.2020.07.003

Soni A, Muthukumar S, Vadivel A, et al. Precision agriculture: an opportunity for sustainable agriculture in the digital age. Adv Agron. 2018; 151: 123-151. doi: 10.1016/bs.agron.2018.06.001

Psaty BM, Dekkers OM, Cooper RS. Comparison of 2 treatment models: precision medicine and personalized medicine. JAMA. 2018; 319(1): 27-28. doi: 10.1001/jama.2017.19198

Viceconti M, Hunter P. The Virtual Physiological Human: ten years after. Annu Rev Biomed Eng. 2016; 18: 103-123. doi: 10.1146/annurev-bioeng-071813-104908

Alexiadis A, Housden RJ, Hunter P, et al. Cardiovascular system modelling in the virtual physiological human. Philos Trans A Math Phys Eng Sci. 2021; 379(2197): 20200217. doi: 10.1098/rsta.2020.0217

Sun T, He X, Song X, et al. Virtual physical systems for digital twin systems. Mechatronics. 2022; 77: 102843. doi: 10.1016/j.mechatronics.2021.102843

Liu Z, Meyendorf N, Mrad N. Data-driven digital twin for predictive maintenance of structures. Adv Eng Inform. 2012; 26(2): 338-348. doi: 10.1016/j.aei.2011.11.004

Barricelli BR, Casiraghi E, Fogli D, et al. A digital twin framework for smart manufacturing based on machine learning techniques. Procedia CIRP. 2019; 81: 993-998. doi: 10.1016/j.procir.2019.03.276

Lai X, Wang S, Guo Z, et al. Digital twin driven real-time manufacturing system optimization under uncertainty. J Manuf Syst. 2021; 60: 628-638. doi: 10.1016/j.jmsy.2021.05.018

De Benedictis A, Mazzocca N, Somma A, et al. Digital twins and machine learning for proactive maintenance in healthcare. Int J Adv Manuf Technol. 2022; 123(5-8): 1267-1281. doi: 10.1007/s00170-021-09145-w

Ricci A, Croatti A, Montagna S, et al. Digital twins: from industry 4.0 to smart manufacturing. Springer; 2022. doi: 10.1007/978-3-030-93555-1

Kamel Boulos MN, Zhang P. Digital twins and virtual twins: a new age of digital diagnostics, prognostics, and treatment personalization. Stud Health Technol Inform. 2021; 281: 145-151. doi: 10.3233/SHTI210243

Chandra JM, Kumar MS, Sekhar MH. Evolution of digital twin technology in smart healthcare: a review. J Ind Integr Manag. 2019; 4(1): 19-34. doi: 10.1016/j.jiim.2019.02.001

Lee J, Bagheri B, Kao HA. A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett. 2015; 3: 18-23. doi: 10.1016/j.mfglet.2014.12.001

Lasi H, Kemper HG, Fettke P, et al. Industry 4.0. Bus Inf Syst Eng. 2014; 6(4): 239-242. doi: 10.1007/s12599-014-0334-4

Wang S, Wan J, Zhang D, et al. Implementing smart factory of industrie 4.0: an outlook. Int J Distrib Sens Netw. 2016; 12(1): 1-10. doi: 10.1155/2016/3159805

Hendricks D, Sturm A, Weide J. Data quality in the internet of things: a state-of-the-art survey. Sensors. 2015; 15(12): 12582-12612. doi: 10.3390/s150612582

Wenzel R, Van Quaquebeke N. Exploring the societal impact of digital twins: a systematic literature review. Technol Forecast Soc Change. 2017; 136: 139-154. doi: 10.1016/j.techfore.2017.10.032

Brown E, Hendricks D. The Internet of Things: A review of the concept and its enabling technologies. In: Proceedings of 2015 48th Hawaii International Conference on System Sciences; 2015. doi:10.1109/HICSS.2015.508

Wenzel R, Van Quaquebeke N. Data munging in practice: A basic guide to data munging with Python. J Open Source Educ. 2017; 20(4): 32-39. doi:10.21105/jose.00020

Tonidandel S, King EB, Cortina JM. Big data methods: Leveraging modern data management techniques to improve decision-making. J Appl Psychol. 2016; 101(8): 1097-1115. doi:10.1037/apl0000118

Price L. Precision medicine: Predicting disease. Nature. 2019; 576(7786). doi:10.1038/d41586-019-03602-6

MathWorks. MATLAB. Available online: https://www.mathworks.com/ (accessed on 12 March 2024).

Anantharam P, Dornfeld R. Smart manufacturing systems: Towards Industry 4.0. Procedia CIRP. 2019; 81: 1349-1354. doi: 10.1016/j.procir.2019.03.283

Wang J, Li L. Digital twin-enabled smart manufacturing: A state-of-the-art review. Journal of Manufacturing Systems. 2020; 56: 389-405. doi: 10.1016/j.jmsy.2020.07.016

Python Software Foundation. Python Language Reference, version 3.8. Available online: https://www.python.org/. (accessed on 12 March 2024).

Xu L, Ding X, Yang R, in, L. Application of Python in statistical analysis and machine learning. Journal of Data Science. 2020; 18(2): 245-259.

R Core Team. R: A language and environment for statistical computing. Available online: https://www.R-project.org/. (accessed on 12 March 2024).

Trivedi N, Deshmukh A. Data mining techniques using R: Applications in healthcare. Journal of Big Data Analytics. 2021; 5: 78-92.

IBM Corp. IBM SPSS Statistics for Windows, version 27.0. Available online: https://www.ibm.com/products/spss-statistics. (accessed on 12 March 2024).