Privacy, Data Security, and the Acceptance of AAL-Systems – A User-Specific Perspective

  • Julia van HeekEmail author
  • Simon Himmel
  • Martina Ziefle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10297)


Rising care needs, higher proportions of older, diseased, or disabled people, and an increasing deficiency of qualified care staff due to demographic changes are major challenges in western societies. Ambient Assisted Living (AAL) technologies represent one approach to face these challenges. Besides technological developments and implementations, focusing on user acceptance (including diverse stakeholder perspectives) is important for a successful rollout. As the most previous studies focus on age-related issues, this paper emphasizes especially on people with care needs due to a disability. In particular the acceptance of an AAL system is investigated considering the trade-off between perceived benefits (e.g., increasing autonomy) and perceived barriers (e.g., invasion in privacy, to “abandon” data security). Using a quantitative online questionnaire, decisive use conditions are identified, and the trade-offs and AAL-acceptance are evaluated comparing four user groups: “healthy people” without experiences with disabilities, disabled people, family members, and professional care givers. Results indicate that experience with disabilities influence the acceptance and relevant use conditions of AAL systems as well as the trade-offs between benefits and barriers. The results demonstrate the relevance to include diverse user groups (age, diseases, disabilities) and their specific needs and wishes into the design and evaluation process of AAL technologies.


Ambient assisted living (AAL) technologies Technology acceptance User diversity Privacy & data security Disabilities & care needs 



The authors thank all participants for their patience and openness to share opinions on novel technologies. Furthermore, the authors want to thank Lisa Portz for research assistance. This work was funded by the German Federal Ministry of Education and Research project Whistle (16SV7530).


  1. 1.
    Bloom, D.E., Canning, D.: Global demographic change: dimensions and economic significance. National Bureau of Economic Research, Report no. 10817 (2004).
  2. 2.
    Walker, A., Maltby, T.: Active ageing: A strategic policy solution to demographic ageing in the European union. Int. J. Soc. Welf. 21, 117–130 (2012)CrossRefGoogle Scholar
  3. 3.
    Shaw, J.E., Sicree, R.A., Zimmet, P.Z.: Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res. Clin. Pract. 87(1), 4–14 (2010)CrossRefGoogle Scholar
  4. 4.
    Wild, S., Roglic, G., Green, A., Sicree, R., King, H.: Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27(5), 1047–1053 (2004)CrossRefGoogle Scholar
  5. 5.
    Roger, V.L., Go, A.S., Lloyd-Jones, D.M., Adams, R.J., Berry, J.D., Brown, T.M.: Heart disease and stroke statistics–2011 update: a report from the American heart association. Circulation 123(4), e18–209 (2011)CrossRefGoogle Scholar
  6. 6.
    Geenen, S.J., Powers, L.E., Sells, W.: Understanding the role of health care providers during the transition of adolescents with disabilities and special health care needs. J. Adolesc. Health 32(3), 225–233 (2003)CrossRefGoogle Scholar
  7. 7.
    Poore, C.: Disability in Twentieth-Century German Culture. University of Michigan Press, Ann Arbor (2007)CrossRefGoogle Scholar
  8. 8.
    Schmitt, J.M.: Innovative medical technologies help ensure improved patient care and cost-effectiveness. J. Med. Mark. Device Diagn. Pharm. Mark. 2(2), 174–178 (2002)CrossRefGoogle Scholar
  9. 9.
    Cheng, J., Chen, X., Shen, M.: A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals. IEEE J. Biomed. Health Inf. 17(1), 38–45 (2013)CrossRefGoogle Scholar
  10. 10.
    Baig, M.M., Gholamhosseini, H.: Smart health monitoring systems: an overview of design and modeling. J. Med. Syst. 37(2), 9898 (2013)CrossRefGoogle Scholar
  11. 11.
    Rashidi, P., Mihailidis, A.: A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inf. 17(3), 579–590 (2013)CrossRefGoogle Scholar
  12. 12.
    Fuchsberger, V.: Ambient assisted living: elderly people’s needs and how to face them. In: Proceedings of the 1st ACM International Workshop on Semantic Ambient Media Experiences, pp. 21–24. ACM (2008)Google Scholar
  13. 13.
    Demiris, G., Hensel, B.K., Skubic, M., Rantz, M.: Senior residents’ perceived need of and preferences for “smart home” sensor technologies. Int. J. Technol. Assess. Health Care 24(1), 120–124 (2008)CrossRefGoogle Scholar
  14. 14.
    Wilkowska, W., Gaul, S., Ziefle, M.: A small but significant difference – the role of gender on acceptance of medical assistive technologies. In: Leitner, G., Hitz, M., Holzinger, A. (eds.) USAB 2010. LNCS, vol. 6389, pp. 82–100. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-16607-5_6 CrossRefGoogle Scholar
  15. 15.
    Casenio: Casenio - intelligente Hilfe- and Komfortsysteme [intelligent support and comfort systems] (2016). Webpage
  16. 16.
    Essence: Smart Care - Care@Home Product Suite (2016). Webpage
  17. 17.
    EarlySense: EarlySense all-in-one system. webpage (2016).
  18. 18.
    Tunstall: Tunstall - solutions for healthcare professionals (2016). Webpage
  19. 19.
    Sixsmith, A., Meuller, S., Lull, F., Klein, M., Bierhoff, I., Delaney, S.: SOPRANO – an ambient assisted living system for supporting older people at home. In: Mokhtari, M., Khalil, I., Bauchet, J., Zhang, D., Nugent, C. (eds.) Ambient Assistive Health and Wellness Management in the Heart of the City, pp. 233–236. Springer, Berlin Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Gövercin, M., Meyer, S., Schellenbach, M., Steinhagen-Thiessen, E., Weiss, B., Haesner, M.: SmartSenior@home: acceptance of an integrated ambient assisted living system. Results of a clinical field trial in 35 households. Inf. Health Soc. Care 25, 1–18 (2016)Google Scholar
  21. 21.
    Kleinberger, T., Becker, M., Ras, E., Holzinger, A., Müller, P.: Ambient intelligence in assisted living: enable elderly people to handle future interfaces. In: Stephanidis, C. (ed.) UAHCI 2007. LNCS, vol. 4555, pp. 103–112. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-73281-5_11 CrossRefGoogle Scholar
  22. 22.
    Rogers, E.M.: Diffusion of Innovations, 4th edn. Free Press, New York (1995)Google Scholar
  23. 23.
    Beringer, R., Sixsmith, A., Campo, M., Brown, J., McCloskey, R.: The “acceptance” of ambient assisted living: developing an alternate methodology to this limited research lens. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds.) ICOST 2011. LNCS, vol. 6719, pp. 161–167. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21535-3_21 CrossRefGoogle Scholar
  24. 24.
    Sun, H., De Florio, V., Gui, N., Blondia, C.: The missing ones: key ingredients towards effective ambient assisted living systems. J. Ambient Intell. Smart Environ. 2(2), 109–120 (2010)Google Scholar
  25. 25.
    Wilkowska, W., Ziefle, M., Himmel, S.: Perceptions of personal privacy in smart home technologies: do user assessments vary depending on the research method? In: Tryfonas, T., Askoxylakis, I. (eds.) HAS 2015. LNCS, vol. 9190, pp. 592–603. Springer, Cham (2015). doi: 10.1007/978-3-319-20376-8_53 CrossRefGoogle Scholar
  26. 26.
    Kowalewski, S., Wilkowska, W., Ziefle, M.: Accounting for user diversity in the acceptance of medical assistive technologies. In: Szomszor, M., Kostkova, P. (eds.) eHealth 2010. LNICSSITE, vol. 69, pp. 175–183. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23635-8_22 CrossRefGoogle Scholar
  27. 27.
    Demiris, G., Rantz, M., Aud, M., Marek, K., Tyrer, H., Skubic, M.: Older adults’ attitudes towards and perceptions of “smart home” technologies: a pilot study. Med. Inf. Internet Med. 29(2), 87–94 (2004)CrossRefGoogle Scholar
  28. 28.
    Ziefle, M., Himmel, S., Wilkowska, W.: When your living space knows what you do: acceptance of medical home monitoring by different technologies. In: Holzinger, A., Simonic, K.-M. (eds.) Information Quality in e-Health, pp. 607–624. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  29. 29.
    Beringer, R., Sixsmith, A., Campo, M., Brown, J., McCloskey, R.: The “acceptance” of ambient assisted living: developing an alternate methodology to this limited research lens. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds.) ICOST 2011. LNCS, vol. 6719, pp. 161–167. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21535-3_21 CrossRefGoogle Scholar
  30. 30.
    Himmel, S., Ziefle, M.: Smart home medical technologies: users’ requirements for conditional acceptance. i-Com 15(1), 39–50 (2016)CrossRefGoogle Scholar
  31. 31.
    Brauner, P., Holzinger, A., Ziefle, M.: Ubiquitous computing at its best: serious exercise games for older adults in ambient assisted living environments–a technology acceptance perspective. EAI Endorsed Trans. Serious Games 15, 1–12 (2015)Google Scholar
  32. 32.
    Harris, J.: The use, role and application of advanced technology in the lives of disabled people in the UK. Disabil. Soc. 25(4), 427–439 (2010)CrossRefGoogle Scholar
  33. 33.
    Gentry, T.: Smart homes for people with neurological disability: state of the art. NeuroRehabil. 25(3), 209–217 (2009)Google Scholar
  34. 34.
    López, S.A., Corno, F., Russis, L.D.: Supporting caregivers in assisted living facilities for persons with disabilities: a user study. Univ. Access Inf. Soc. 14(1), 133–144 (2015)CrossRefGoogle Scholar
  35. 35.
    Mortenson, W.B., Demers, L., Fuhrer, M.J., Jutai, J.W., Lenker, J., DeRuyter, F.: Effects of an assistive technology intervention on older adults with disabilities and their informal caregivers: an exploratory randomized controlled trial. Am. J. Phys. Med. Rehabil. Assoc. Acad. Physiatr. 92(4), 297–306 (2013)CrossRefGoogle Scholar
  36. 36.
    Beier, G.: Locus of control when interacting with technology (Kontrollüberzeugungen im {U}mgang mit {T}echnik). Rep. Psychol. 24, 684–693 (1999)Google Scholar
  37. 37.
    Wilkowska, W.: Acceptance of eHealth Technology in Home Environments: Advanced Studies on User Diversity in Ambient Assisted Living. Apprimus Verlag, Aachen (2015)Google Scholar
  38. 38.
    Broadbent, E., Tamagawa, R., Patience, A., Knock, B., Kerse, N., Day, K.: Attitudes towards health-care robots in a retirement village. Australas. J. Ageing 31(2), 115–120 (2012)CrossRefGoogle Scholar
  39. 39.
    Dorsten, A.-M., Sifford, K.S., Bharucha, A., Mecca, L.P., Wactlar, H.: Ethical perspectives on emerging assistive technologies: insights from focus groups with stakeholders in long-term care facilities. J. Empir. Res. Hum. Res. Ethics 4(1), 25–36 (2009)CrossRefGoogle Scholar
  40. 40.
    Himmel, S., Ziefle, M., Arning, K.: From living space to urban quarter: acceptance of ICT monitoring solutions in an ageing society. In: Kurosu, M. (ed.) Human-Computer Interaction Users and Contexts of Use, pp. 49–58. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  41. 41.
    Ajzen, I., Fishbein, M.: Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs (1980)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Human-Computer Interaction Center, RWTH AachenAachenGermany

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