Ambient Intelligence from Senior Citizens’ Perspectives: Understanding Privacy Concerns, Technology Acceptance, and Expectations

  • Florian Kirchbuchner
  • Tobias Grosse-Puppendahl
  • Matthias R. Hastall
  • Martin Distler
  • Arjan Kuijper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9425)


Especially for seniors, Ambient Intelligence can provide assistance in daily living and emergency situations, for example by automatically recognizing critical situations. The use of such systems may involve trade-offs with regard to privacy, social stigmatization, and changes of the well-known living environment. This raises the question of how older adults perceive restrictions of privacy, accept technology, and which requirements are placed on Ambient Intelligent systems. In order to better understand the related concerns and expectations, we surveyed 60 senior citizens. The results show that experience with Ambient Intelligence increases technology acceptance and reduces fears regarding privacy violations and insufficient system reliability. While participants generally tolerate a monitoring of activities in their home, including bathrooms, they do not accept commercial service providers as data recipients. A comparison between four exemplary systems shows that camera-based solutions are perceived with much greater fears than wearable emergency solutions. Burglary detection was rated as similarly important assigned as health features, whereas living comfort features were considered less useful.


Privacy concerns Older adults Perception of privacy Technology acceptance 


  1. 1.
    Adams, A.: Users’ perception of privacy in multimedia communication. In: CHI 1999, Extended Abstracts on Human Factors in Computing Systems, pp. 53–54. ACM (1999)Google Scholar
  2. 2.
    Aminzadeh, F., Edwards, N.: Exploring seniors’ views on the use of assistive devices in fall prevention. Public Health Nurs. 15(4), 297–304 (1998)CrossRefGoogle Scholar
  3. 3.
    Beckwith, R.: Designing for ubiquity: the perception of privacy. IEEE Pervasive Comput. 2(2), 40–46 (2003)CrossRefGoogle Scholar
  4. 4.
    van den Broek, G., Cavallo, F., Wehrmann, C.: AALIANCE Ambient Assisted Living Roadmap, vol. 6. IOS press, Leiden (2010)Google Scholar
  5. 5.
    Chernbumroong, S., Atkins, A.S., Yu, H.: Perception of smart home technologies to assist elderly people. In: The Fourth International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2010), pp. 90–97 (2010)Google Scholar
  6. 6.
    Christensen, K., Doblhammer, G., Rau, R., Vaupel, J.W.: Ageing populations: the challenges ahead. Lancet 374(9696), 1196–1208 (2009)CrossRefGoogle Scholar
  7. 7.
    Coughlin, J.F., D’Ambrosio, L.A., Reimer, B., Pratt, M.R.: Older adult perceptions of smart home technologies: implications for research, policy & market innovations in healthcare. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, EMBS 2007, pp. 1810–1815. IEEE (2007)Google Scholar
  8. 8.
    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(01), 120–124 (2008)CrossRefGoogle Scholar
  9. 9.
    Demiris, G., Rantz, M.J., Aud, M.A., Marek, K.D., Tyrer, H.W., Skubic, M., Hussam, A.A.: Older adults’ attitudes towards and perceptions of ‘smart home’ technologies: a pilot study. Inf. Health Soc. Care 29(2), 87–94 (2004)CrossRefGoogle Scholar
  10. 10.
    Edgcomb, A., Vahid, F.: Privacy perception and fall detection accuracy for in-home video assistive monitoring with privacy enhancements. ACM SIGHIT Rec. 2(2), 6–15 (2012)CrossRefGoogle Scholar
  11. 11.
    Giannakouris, K.: Ageing characterises the demographic perspectives of the european societies. Stat. Focus 72, 2008 (2008)Google Scholar
  12. 12.
    Grosse-Puppendahl, T., Berlin, E., Borazio, M.: Enhancing accelerometer-based activity recognition with capacitive proximity sensing. In: Paternò, F., de Ruyter, B., Markopoulos, P., Santoro, C., van Loenen, E., Luyten, K. (eds.) AmI 2012. LNCS, vol. 7683, pp. 17–32. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  13. 13.
    Larizza, M.F., Zukerman, I., Bohnert, F., Busija, L., Bentley, S.A., Russell, R.A., Rees, G.: In-home monitoring of older adults with vision impairment: exploring patients’, caregivers’ and professionals’ views. J. Am. Med. Inf. Assoc. 21(1), 56–63 (2014)CrossRefGoogle Scholar
  14. 14.
    Marcellini, F., Mollenkopf, H., Spazzafumo, L., Ruoppila, I.: Acceptance and use of technological solutions by the elderly in the outdoor environment: findings from a european survey. Zeitschrift Fur Gerontologie Und Geriatrie 33(3), 169–177 (2000)CrossRefGoogle Scholar
  15. 15.
    McCreadie, C., Tinker, A.: The acceptability of assistive technology to older people. Ageing Soc. 25(01), 91–110 (2005)CrossRefGoogle Scholar
  16. 16.
    Melenhorst, A.S., Rogers, W.A., Bouwhuis, D.G.: Older adults’ motivated choice for technological innovation: evidence for benefit-driven selectivity. Psychol. Aging 21(1), 190 (2006)CrossRefGoogle Scholar
  17. 17.
    Noury, N., Fleury, A., Rumeau, P., Bourke, A., Laighin, G., Rialle, V., Lundy, J.: Fall detection-principles and methods. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007, pp. 1663–1666. IEEE (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Florian Kirchbuchner
    • 1
  • Tobias Grosse-Puppendahl
    • 1
  • Matthias R. Hastall
    • 2
  • Martin Distler
    • 3
  • Arjan Kuijper
    • 3
  1. 1.Fraunhofer Institute for Computer Graphics Research IGDDarmstadtGermany
  2. 2.Faculty of Rehabilitation SciencesTU Dortmund UniversityDortmundGermany
  3. 3.Faculty of Computer ScienceTechnische Universität DarmstadtDarmstadtGermany

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