Skip to main content

Who Will Own Our Global Digital Twin: The Power of Genetic and Biographic Information to Shape Our Lives

  • Chapter
  • First Online:
The Digital Twin of Humans

Abstract

Today, it is possible to collect and connect large amounts of digital data from various sources and life domains. This chapter examines the potential and the risks of this development from an interdisciplinary perspective. It defines the ‘global digital twin’ of a human being as the sum of all digitally stored information and predictive knowledge about a person. It points out that, compared to the digital twin of a machine, the human global digital twin is far more complex because it comprises the genetic code and the biographic code of a person. The genetic code contains not only a simple ‘construction plan’ but also hereditary information, in a form that is difficult to read. The biographic code contains all other information that can be assembled about a person, which is obtained via data from cameras, microphones, or other sensors, as well as general personal information. When the growing wealth of information concerning the genetic code and the biographical code is properly utilised, insights from biology and the behavioural sciences may be used to predict personal events such as health problems, job resignations, or even crimes. Because our own interests and those of private firms are partly in conflict over the use of this powerful knowledge, it is still unclear whether the global digital twins of humans will become a liberating or disciplining force for citizens. On the one hand, human beings are not machines: They are aware of their digital twin and therefore are able to influence it throughout their lives. Because of their free will, human beings are in general difficult to predict. Dystopias of full control over individual behaviour are therefore unlikely to materialise. On the other hand, private firms are beginning to take advantage of the available digital twins of humans by monopolising data access and by commercialising predictive knowledge. This is problematic because, unlike machines, human beings cannot only benefit from but also suffer due to their digital twins as they attempt to shape their own lives. We illustrate these issues with some examples and arrive at two conclusions: It is in the public interest for people to be granted more property rights over their personal global digital twins, and publicly funded research needs to become more interdisciplinary, much like private firms that have already begun to perform interdisciplinary research.

S. Pilz and T. Hellweg contributed equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 59.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    These nucleotides are distinguished by their different bases, adenine (A), cytosine (C), guanine (G), and thymine (T).

  2. 2.

    Note: This is a highly simplified explanation.

  3. 3.

    Gartner summarises several technologies that are part of a leading trend called the ‘digital me’ (see Gartner (2020)). It contains, e.g., the digital twin of a person, citizen twins, health passports, and bidirectional brain-machine interfaces.

  4. 4.

    The readiness to donate blood decreases if subjects feel that they are under pressure.

  5. 5.

    Employees reduce their proactive behaviour if they feel restricted by managers’ instructions.

References

  • 3DS. (2021). The Living Heart Project. https://www.3ds.com/products-services/simulia/solutions/life-sciences-healthcare/the-living-heart-project/

  • Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections.

    Google Scholar 

  • Alderson, J., & Johnson, W. (2016). The personalised ‘digital athlete’: An evolving vision for the capture, modelling and simulation, of on-field athletic performance. In ISBS-conference proceedings archive.

    Google Scholar 

  • Bagaria, N., Laamarti, F., Badawi, H. F., Albraikan, A., Martinez Velazquez, R. A., & El Saddik, A. (2020). Health 4.0: Digital twins for health and well-being. In Connected health in smart cities (pp. 143–152). Springer.

    Google Scholar 

  • Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access, 7, 167653–167671. https://doi.org/10.1109/ACCESS.2019.2953499

  • Barricelli, B. R., Casiraghi, E., Gliozzo, J., Petrini, A., & Valtolina, S. (2020). Human digital twin for fitness management. IEEE Access, 8, 26637–26664. https://doi.org/10.1109/ACCESS.2020.2971576

  • Baskaran, S., Niaki, F. A., Tomaszewski, M., Gill, J. S., Chen, Y., Jia, Y., Mears, L., & Krovi, V. (2019). Digital human and robot simulation in automotive assembly using Siemens process simulate: A feasibility study. Procedia Manufacturing, 34, 986–994.

    Article  Google Scholar 

  • Berisha-Gawlowski, A., Caruso, C., & Harteis, C. (2021). The concept of a digital twin and its potential for learning organizations. Digital transformation of learning organizations (pp. 95–114). Cham: Springer.

    Chapter  Google Scholar 

  • Bilal, M., Chaudhry, S., Amber, H., Shahid, M., Aslam, S., & Shahzad, K. (2021). Entrepreneurial leadership and employees’ proactive behaviour: Fortifying self determination theory. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 176.

    Article  Google Scholar 

  • Billett, S., Harteis, C., & Gruber, H. (2018). Developing occupational expertise through everyday work activities and interactions. The Cambridge handbook of expertise and expert performance (pp. 105–126).

    Google Scholar 

  • Bruynseels, K., Santoni de Sio, F., & van den Hoven, J. (2018). Digital twins in health care: Ethical implications of an emerging engineering paradigm. Frontiers in Genetics, 9, 31. https://doi.org/10.3389/fgene.2018.00031

  • Bryndin, E. (2019a). Collaboration robots with artificial intelligence (AI) as digital doubles of person for communication in public life and space. Budapest International Research in Exact Sciences (BirEx-Journal), 1(4), 1–11.

    Google Scholar 

  • Bryndin, E. (2019b). Robots with artificial intelligence and spectroscopic sight in hi-tech labor market. International Journal of Systems Science and Applied Mathematic, 4(3), 31–37.

    Article  Google Scholar 

  • Buchanan, J. M., & Yoon, Y. J. (2000). Symmetric tragedies: Commons and anticommons. The Journal of Law and Economics, 43(1), 1–14.

    Article  Google Scholar 

  • Bush, V. (1945). As we may think. The Atlantic. https://www.theatlantic.com/magazine/archive/1945/07/as-we-may-think/303881/

  • Chakshu, N. K., Sazonov, I., & Nithiarasu, P. (2020). Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis. Biomechanics and Modeling in Mechanobiology. https://doi.org/10.1007/s10237-020-01393-6

  • Consortium, E. P., et al. (2012). An integrated encyclopedia of DNA elements in the human genome. Nature, 489(7414), 57.

    Google Scholar 

  • Croatti, A., Gabellini, M., Montagna, S., & Ricci, A. (2020). On the integration of agents and digital twins in healthcare. Journal of Medical Systems, 44(9).

    Google Scholar 

  • Dahm, R. (2008). Discovering DNA: Friedrich Miescher and the early years of nucleic acid research. Human Genetics, 122(6), 565–581.

    Article  Google Scholar 

  • Davis, C. A., Hitz, B. C., Sloan, C. A., Chan, E. T., Davidson, J. M., Gabdank, I., Hilton, J. A., Jain, K., Baymuradov, U. K., Narayanan, A. K., et al. (2018). The encyclopedia of DNA elements (ENCODE): Data portal update. Nucleic Acids Research, 46(D1), D794–D801.

    Article  Google Scholar 

  • Deci, E. L., & Ryan, R. M. (2012). Self-determination theory. In Van Lange, P. A. M., Kruglanski, A. W., & Higgins, E. T. (eds) Handbook of theories of social psychology (vol. 1).

    Google Scholar 

  • Dlouhy, K., & Froidevaux, A. (2021). Evolution of STEM professionals’ careers upon graduation and occupational turnoverover time. In Presented at workshop of WK personal 2021, Düsseldorf.

    Google Scholar 

  • Dorrer, M. (2020). The digital twin of the business process model. Journal of Physics: Conference Series, 1679, 032096. IOP Publishing.

    Google Scholar 

  • Egger, G., Liang, G., Aparicio, A., & Jones, P. A. (2004). Epigenetics in human disease and prospects for epigenetic therapy. Nature, 429(6990), 457–463.

    Article  Google Scholar 

  • El Saddik, A. (2018). Digital twins: The convergence of multimedia technologies. IEEE MultiMedia, 25(2), 87–92. https://doi.org/10.1109/MMUL.2018.023121167

  • Elgan, M. (2016). Lifelogging is dead (for now). https://www.computerworld.com/article/3048497/lifelogging-is-dead-for-now.html

  • Engels, G. (2020). Der digitale fußabdruck, schatten oder zwilling von maschinen und menschen. Gruppe Interaktion Organisation Zeitschrift für Angewandte Organisationspsychologie (GIO), 51(3), 363–370. https://doi.org/10.1007/s11612-020-00527-9

  • Ericsson, K. A., & Charness, N. (1994). Expert performance: Its structure and acquisition. American Psychologist, 49(8), 725.

    Article  Google Scholar 

  • Erol, T., Mendi, A. F., & Doğan, D. (2020). The digital twin revolution in healthcare. In 2020 4th international symposium on multidisciplinary studies and innovative technologies (ISMSIT) (pp. 1–7). IEEE.

    Google Scholar 

  • Fraga, M. F., Ballestar, E., Paz, M. F., Ropero, S., Setien, F., Ballestar, M. L., Heine-Suñer, D., Cigudosa, J. C., Urioste, M., Benitez, J., et al. (2005). Epigenetic differences arise during the lifetime of monozygotic twins. Proceedings of the National Academy of Sciences, 102(30), 10604–10609.

    Article  Google Scholar 

  • Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358

  • Gartner. (2020). Gartner hype cycle for emerging technologies 2020. https://www.gartner.com/smarterwithgartner/5-trends-drive-the-gartner-hype-cycle-for-emerging-technologies-2020

  • GDPR. (2016). EU General Data Protection Regulation (GDPR): Regulation (EU) 2016/679.

    Google Scholar 

  • Giesen, C. (2019). Ein ganzes land als testgelände. Süddeutsche Zeitung. https://www.sueddeutsche.de/politik/china-ein-ganzes-land-als-testgelaende-1.4664052

  • Glaessgen, E., & Stargel, D. (2012). 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, 20th AIAA/ASME/AHS adaptive structures conference, 14th AIAA. Reston, Virginia: American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2012-1818

  • Gámez Díaz, R., Yu, Q., Ding, Y., Laamarti, F., & El Saddik, A. (2020). Digital twin coaching for physical activities: A survey. Sensors, 20(20), 5936.

    Google Scholar 

  • Gomerova, A., Volkov, A., Muratchaev, S., Lukmanova, O., & Afonin, I. (2021). Digital twins for students: Approaches, advantages and novelty. In 2021 IEEE conference of russian young researchers in electrical and electronic engineering (ElConRus) (pp. 1937–1940). https://doi.org/10.1109/ElConRus51938.2021.9396360

  • Graessler, I., & Poehler, A. (2018). Intelligent control of an assembly station by integration of a digital twin for employees into the decentralized control system. Procedia Manufacturing, 24, 185–189. https://doi.org/10.1016/j.promfg.2018.06.041

  • Grieves, M. (2015). Digital twin: Manufacturing excellence through virtual factory replication. Whitepaper.

    Google Scholar 

  • Hafez, W. (2020). Human digital twin: Enabling human-multi smart machines collaboration. In Y. Bi, R. Bhatia, & S. Kapoor (Eds.), Intelligent systems and applications (pp. 981–993). Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Hafez, W. (2020b). Human digital twins: Two-layer machine learning architecture for intelligent human-machine collaboration. In International conference on intelligent human systems integration (pp. 627–632). Springer.

    Google Scholar 

  • Harteis, C., & Billett, S. (2013). Intuitive expertise: Theories and empirical evidence. Educational Research Review, 9, 145–157.

    Article  Google Scholar 

  • Heinke, A. (2021). How humans and machines interact. In S. Güldenberg, E. Ernst, & K. North (Eds.), Managing work in the digital economy: Challenges, strategies and practices for the next decade (pp. 21–39). Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Hess, C., & Ostrom, E. (2003). Ideas, artifacts, and facilities: Information as a common-pool resource. Law and Contemporary Problems, 66(1/2), 111–145.

    Google Scholar 

  • Hintz, A., Dencik, L., & Wahl-Jorgensen, K. (2018). Digital citizenship in a datafied society. Wiley.

    Google Scholar 

  • Huypens, P., Sass, S., Wu, M., Dyckhoff, D., Tschöp, M., Theis, F., Marschall, S., de Angelis, M. H., & Beckers, J. (2016). Epigenetic germline inheritance of diet-induced obesity and insulin resistance. Nature Genetics, 48(5), 497–499.

    Article  Google Scholar 

  • Jimenez, J. I., Jahankhani, H., & Kendzierskyj, S. (2020). Health care in the cyberspace: Medical cyber-physical system and digital twin challenges. In Digital twin technologies and smart cities (pp. 79–92). Springer.

    Google Scholar 

  • Johnson, W. R., Mian, A., Donnelly, C. J., Lloyd, D., & Alderson, J. (2018). Predicting athlete ground reaction forces and moments from motion capture. Medical & Biological Engineering & Computing, 56(10), 1781–1792.

    Article  Google Scholar 

  • Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52. https://doi.org/10.1016/j.cirpj.2020.02.002

  • Joseph, A., Kruger, K., & Basson, A. H. (2020). An aggregated digital twin solution for human-robot collaboration in Industry 4.0 environments. In International workshop on service orientation in holonic and multi-agent manufacturing (pp. 135–147). Springer.

    Google Scholar 

  • Kemény, Z., Beregi, R., Nacsa, J., Glawar, R., & Sihn, W. (2018). Expanding production perspectives by collaborating learning factories–Perceived needs and possibilities. Procedia Manufacturing, 23, 111–116.

    Article  Google Scholar 

  • Kesti M (2021) The digital twin of an organization by utilizing reinforcing deep learning. In Artificial neural networks and deep learning-Applications and perspective, IntechOpen.

    Google Scholar 

  • Kshetri, N. (2020). China’s social credit system: Data, algorithms and implications. IT Professional, 22(2), 14–18.

    Article  Google Scholar 

  • Laamarti, F., Badawi, H. F., Ding, Y., Arafsha, F., Hafidh, B., & Saddik, A. E. (2020). An ISO/IEEE 11073 standardized digital twin framework for health and well-being in smart cities. IEEE Access, 8, 105950–105961. https://doi.org/10.1109/ACCESS.2020.2999871

  • Labonté, B., Suderman, M., Maussion, G., Navaro, L., Yerko, V., Mahar, I., Bureau, A., Mechawar, N., Szyf, M., Meaney, M. J., et al. (2012). Genome-wide epigenetic regulation by early-life trauma. Archives of General Psychiatry, 69(7), 722–731.

    Article  Google Scholar 

  • Lim, K. Y. H., Zheng, P., & Chen, C. H. (2020). A state-of-the-art survey of digital twin: Techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31, 1313–1337. https://doi.org/10.1007/s10845-019-01512-w

  • Man, K., & Damasio, A. (2019). Homeostasis and soft robotics in the design of feeling machines. Nature Machine Intelligence, 1(10), 446–452.

    Article  Google Scholar 

  • Matusiewicz, D., Puhalac, V., & Werner, J. A. (2018). Avatare im gesundheitswesen. https://www.youtube.com>watch<v=g7Bxm60B-kc

    Google Scholar 

  • Microsoft Research. (2017). Mylifebits - Microsoft Research. https://www.microsoft.com/en-us/research/project/mylifebits/

  • Mossberger, K., Tolbert, C. J., & McNeal, R. S. (2007). Digital citizenship: The Internet, society, and participation. MIT Press.

    Google Scholar 

  • NIH. (2018). Genetics vs. genomics fact sheet. https://www.genome.gov/about-genomics/fact-sheets/Genetics-vs-Genomics

  • NIH. (2020). Human genome project FAQ. https://www.genome.gov/human-genome-project/Completion-FAQ

  • Nikolakis, N., Alexopoulos, K., Xanthakis, E., & Chryssolouris, G. (2019). The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor. International Journal of Computer Integrated Manufacturing, 32(1), 1–12.

    Article  Google Scholar 

  • Park, Y. J., & Skoric, M. (2017). Personalized ad in your Google Glass? Wearable technology, hands-off data collection, and new policy imperative. Journal of Business Ethics, 142(1), 71–82.

    Article  Google Scholar 

  • Petzoldt, C., Wilhelm, J., Hoppe, N. H., Rolfs, L., Beinke, T., & Freitag, M. (2020). Control architecture for digital twin-based human-machine interaction in a novel container unloading system. Procedia Manufacturing, 52, 215–220.

    Article  Google Scholar 

  • Popa, E. O., van Hilten, M., Oosterkamp, E., & Bogaardt, M. J. (2021). The use of digital twins in healthcare: Socio-ethical benefits and socio-ethical risks. Life Sciences, Society and Policy, 17(1), 1–25.

    Article  Google Scholar 

  • Rodríguez Aguilar, R., & Marmolejo Saucedo, J. A. (2020). Conceptual framework of digital health public emergency system: Digital twins and multiparadigm simulation.

    Google Scholar 

  • Shengli, W. (2021). Is human digital twin possible? Computer Methods and Programs in Biomedicine Update, 1. https://doi.org/10.1016/j.cmpbup.2021.100014

  • Sun, J., Tian, Z., Fu, Y., Geng, J., & Liu, C. (2021). Digital twins in human understanding: A deep learning-based method to recognize personality traits. International Journal of Computer Integrated Manufacturing, 34(7–8), 860–873. https://doi.org/10.1080/0951192X.2020.1757155

  • Suzuki, M. M., & Bird, A. (2008). DNA methylation landscapes: Provocative insights from epigenomics. Nature Reviews Genetics, 9(6), 465–476.

    Article  Google Scholar 

  • Sweeney, L. (2000). Simple demographics often identify people uniquely. Health (San Francisco), 671(2000), 1–34.

    Google Scholar 

  • Terpsma, R. J., & Hovey, C. B. (2020). Blunt impact brain injury using cellular injury criterion, Technical report. Sandia National Lab (SNL-NM), Albuquerque, NM (United States).

    Google Scholar 

  • Thumfart, K. M., Jawaid, A., Bright, K., Flachsmann, M., & Mansuy, I. M. (2021) Epigenetics of childhood trauma: Long term sequelae and potential for treatment. Neuroscience & Biobehavioral Reviews.

    Google Scholar 

  • Tröbinger, M., Jähne, C., Qu, Z., Elsner, J., Reindl, A., Getz, S., Goll, T., Loinger, B., Loibl, T., Kugler, C., et al. (2021). Introducing GARMI-A service robotics platform to support the elderly at home: Design philosophy, system overview and first results. IEEE Robotics and Automation Letters, 6(3), 5857–5864.

    Article  Google Scholar 

  • Truby, J., & Brown, R. (2021). Human digital thought clones: The Holy Grail of artificial intelligence for big data. Information & Communications Technology Law, 30(2), 140–168.

    Article  Google Scholar 

  • Visholm, A., Grosen, L., Norn, M. T., & Jensen, R. L. (2012). Interdisciplinary research is key to solving society’s problems. DEA, Copenhagen Interdisciplinarity and Sustainability: Shaping Futures.

    Google Scholar 

  • Voigt, I., Inojosa, H., Dillenseger, A., Haase, R., Akgün, K., & Ziemssen, T. (2021). Digital twins for multiple sclerosis. Frontiers in Immunology, 12, 1556.

    Google Scholar 

  • Watson, J. D., & Crick, F. H. (1953). Molecular structure of nucleic acids: A structure for deoxyribose nucleic acid. Nature, 171(4356), 737–738.

    Article  Google Scholar 

  • Wetterstrand, K. A. (2020). DNA sequencing costs: Data from the NHGRI genome sequencing program (GSP). www.genome.gov/sequencingcostsdata

  • Williams, L. A., Sun, J., & Masser, B. (2019). Integrating self-determination theory and the theory of planned behaviour to predict intention to donate blood. Transfusion Medicine, 29, 59–64.

    Article  Google Scholar 

  • Yigitbas, E., Karakaya, K., Jovanovikj, I., & Engels, G. (2021). Enhancing human-in-the-loop adaptive systems through digital twins and VR interfaces. arXiv:2103.10804

  • Zibuschka, J., Ruff, C., Horch, A., & Roßnagel, H. (2020). A human digital twin as building block of open identity management for the Internet of Things. Open Identity Summit 2020.

    Google Scholar 

Download references

Acknowledgements

Sarah Pilz, Talea Hellweg, Christian Harteis, Ulrich Rückert, and Martin Schneider are members of the research programme ‘Design of Flexible Work Environments—Human-Centric Use of Cyber-Physical Systems in Industry 4.0’, which is supported by the North Rhine-Westphalian funding scheme ‘Forschungskolleg’. We would like to thank Marc Wollny for his assistance with literature research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sarah Pilz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Pilz, S., Hellweg, T., Harteis, C., Rückert, U., Schneider, M. (2023). Who Will Own Our Global Digital Twin: The Power of Genetic and Biographic Information to Shape Our Lives. In: Gräßler, I., Maier, G.W., Steffen, E., Roesmann, D. (eds) The Digital Twin of Humans. Springer, Cham. https://doi.org/10.1007/978-3-031-26104-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26104-6_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26103-9

  • Online ISBN: 978-3-031-26104-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics