Skip to main content
SpringerLink
Log in

Privacy Predictive Models for Homecare Patient Sensing

  • Chapter
  • First Online:
Book cover Personal Health Informatics

Part of the book series: Cognitive Informatics in Biomedicine and Healthcare ((CIBH))

  • 359 Accesses

Abstract

The pace of population aging has promoted the development of homecare monitoring systems and assisted living technologies. On the one hand, these technologies are supposed to help patients and the elderly at home to get help in any medical emergencies. On the other hand, such monitoring systems have raised the concern about patients’ privacy. Though privacy-enhancing technologies for homecare sensing have been developed to protect patients’ privacy, there have been few researches on patients’ privacy attitudes towards different homecare sensing technologies, which may impact the practical performance of these sensing systems. Since individuals have different privacy attitudes towards the sensing systems and their needs in health monitoring, it would be interesting for the healthcare service providers and technology vendors to know about patients’ privacy attitudes and how to model them into actionable privacy settings. In this chapter, we discuss the research state of the arts in this area and describe a preliminary study on this topic conducted recently. The chapter includes the following parts: first, an overview of homecare sensing and assisted living technologies; second, patients’ privacy attitudes towards healthcare monitoring and video surveillance systems; third, legal and ethical considerations of using camera for patient monitoring; and finally, our findings from the preliminary study consists of focus group discussions and questionnaire used to collect people’s privacy attitudes, and test results of applying different methods to predict patients’ privacy preferences.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 69.99
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.

    http://www.giraffplus.eu/

References

  • Abbott-Chapman J, Robertson M. Adolescents’ favourite places: redefining the boundaries between private and public space. Space Cult. 2009;12:419–34.

    Article  Google Scholar 

  • Adams A, Cox A (2008) Questionnaires, in-depth interviews and focus groups. Res. Methods Hum.-Comput. Interact.

    Google Scholar 

  • Adelman RD, Tmanova LL, Delgado D, Dion S, Lachs MS. Caregiver burden: a clinical review. JAMA J Am Med Assoc. 2014;311:1052–60.

    Article  Google Scholar 

  • Altuntas A, Unal A, Aslan A, Ozcan M, Kurkcuoglu S, Nalca Y. Facial nerve paralysis in chronic suppurative otitis media: Ankara Numune hospital experience. Auris Nasus Larynx. 1998;25:169–72.

    Article  Google Scholar 

  • Andersson N-B, Hanson E, Magnusson L, Nolan M. Views of family carers and older people of information technology. Br J Nurs Mark Allen Publ. 2002;11:827–31.

    Article  Google Scholar 

  • Annenberg School for Communication. “The tradeoff fallacy: how marketers are misrepresenting American consumers and opening them up to exploitation.” Annenberg School for Communication, 2015. | Annenberg School for Communication. https://www.asc.upenn.edu/news-events/publications/tradeoff-fallacy-how-marketers-are-misrepresenting-american-consumers-and.

  • Anon. (2015) THE GREAT DATA RACE - How commercial utilisation of personal data challenges privacy [Internet]. Norwegian Data Protection Authority (Datatilsynet). https://www.datatilsynet.no/globalassets/global/english/engelskkommersialisering-endelig.pdf.

  • Anon. (2019) Nordmenn og deling av persondata.

    Google Scholar 

  • Art. 13 GDPR, Art. 14 GDPR (n.d.) - Right to be informed. Gen. Data Prot. Regul. GDPR.

    Google Scholar 

  • Anon. (n.d.-a) storbyuniversitetet O- Jürgen Kasper. https://www.oslomet.no/om/ansatt/jurgenka.

  • Anon. (n.d.-b) 1.4. Support Vector Machines—scikit-learn 0.24.2 documentation. https://scikit-learn.org/stable/modules/svm.html.

  • Anon. (n.d.-c) sklearn.linear_model.LogisticRegression—scikit-learn 0.24.2 documentation. https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html.

  • Anon. (n.d.-d) 1.9. Naive Bayes—scikit-learn 0.24.2 documentation. https://scikit-learn.org/stable/modules/naive_bayes.html.

  • Anon. (n.d.-e) sklearn.metrics.mean_squared_error—scikit-learn 0.24.2 documentation. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html.

  • Art. 14 GDPR (n.d.) – Information to be provided where personal data have not been obtained from the data subject. Gen. Data Prot. Regul. GDPR.

    Google Scholar 

  • Art. 22 GDPR (n.d.) – Automated individual decision-making, including profiling. Gen. Data Prot. Regul. GDPR.

    Google Scholar 

  • Art. 5 GDPR (n.d.) – Principles relating to processing of personal data. Gen. Data Prot. Regul. GDPR.

    Google Scholar 

  • Art. 7 GDPR (n.d.) – Conditions for consent. Gen. Data Prot. Regul. GDPR.

    Google Scholar 

  • Assisted Living-prosjektet. In: Assist. Living-Prosjektet. n.d.. https://assistedlivingweb.wordpress.com/.

  • Barth S, de Jong MDT. The privacy paradox—investigating discrepancies between expressed privacy concerns and actual online behavior—a systematic literature review. Telemat Inform. 2017;34:1038–58.

    Article  Google Scholar 

  • Boise L, Wild K, Mattek N, Ruhl M, Dodge HH, Kaye J. Willingness of older adults to share data and privacy concerns after exposure to unobtrusive in-home monitoring. Gerontechnology. 2013;11:428–35.

    Article  Google Scholar 

  • Caine K (2009) Visual sensing devices in home-care systems. In: Proc. First ACM Workshop Secur. Priv. Med. Home-Care Syst. ACM, pp 61–62.

    Google Scholar 

  • Chan M, Campo E, Estève D. Assessment of activity of elderly people using a home monitoring system. Int J Rehabil Res. 2005;28:69–76.

    Article  Google Scholar 

  • Coughlin KW. Medical decision-making in paediatrics: infancy to adolescence. Paediatr Child Health. 2018;23:138–46.

    Article  Google Scholar 

  • Hammer B, Hofmann D, Schleif F-M, Zhu X. Learning vector quantization for (dis-)similarities. Neurocomputing Amst. 2014;131:43–51.

    Article  Google Scholar 

  • iRobot®: Robot vacuum and mop. n.d.. http://www-origin9.irobot.com/.

  • Junestrand S. Being private and public at home. Sweden: Stockholm; 2004.

    Google Scholar 

  • Lawton PM. Aging and performance of home tasks. Hum Factors. 1990;32:527–36.

    Article  Google Scholar 

  • Lincoln S. ‘My bedroom is me’: young people, private space, consumption and the family home 2015. pp 87–106.

    Google Scholar 

  • Lymberis A, Paradiso R (2008) Smart fabrics and interactive textile enabling wearable personal applications: R&D state of the art and future challenges. 2008 30th Annu Int Conf IEEE Eng Med Biol Soc 5270–5273.

    Google Scholar 

  • Magjarevic R. Home care technologies for ambient assisted living. In: 11th Mediterr. Conf. Med. Biomed. Eng. Comput. Berlin, Heidelberg: Springer Berlin Heidelberg; 2007. p. 397–400.

    Google Scholar 

  • Malone TB, Kirkpatrick MJ, Herman RP, Creedon MA, Cohen-Mansfield J, Dutra LA. Electronic memory aids for community-dwelling elderly persons: attitudes, preferences, and potential utilization. J Appl Gerontol. 2005;24:3–20.

    Article  Google Scholar 

  • Mayo Clinic. Parkinson’s disease—Symptoms and causes. In: Mayo Clin. n.d.. https://www.mayoclinic.org/diseases-conditions/parkinsons-disease/symptoms-causes/syc-20376055.

  • Nick M, Becker M. A hybrid approach to intelligent living assistance. In: 7th Int. Conf. Hybrid Intell. Syst. HIS 2007 2007. pp 283–289.

    Google Scholar 

  • Norberg PA, Horne DR, Horne DA. The privacy paradox: personal information disclosure intentions versus behaviors. J Consum Aff. 2007;41:100–26.

    Article  Google Scholar 

  • Palumbo F, Ullberg J, Stimec A, Furfari F, Karlsson L, Coradeschi S. Sensor network infrastructure for a home care monitoring system. Sensors. 2014;14:3833–60.

    Article  Google Scholar 

  • Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825.

    MathSciNet  MATH  Google Scholar 

  • Pounds-Cornish A, Holmes A. The iDorm—a practical deployment of grid technology. In: 2nd IEEEACM Int. Symp. Clust. Comput. Grid CCGRID02. IEEE, 2002; 470–470.

    Google Scholar 

  • Rashidi P, Mihailidis A. A survey on ambient-assisted living tools for older adults. IEEE J Biomed Health Inform. 2013;17:579–90.

    Article  Google Scholar 

  • Smarr C-A, Fausset CB, Rogers WA (2011) Understanding the Potential for Robot Assistance for Older Adults in the Home Environment.

    Google Scholar 

  • Socha R, Kogut B. Urban video surveillance as a tool to improve security in public spaces. Sustain Basel Switz. 2020;12:6210.

    Google Scholar 

  • Thorstensen E (2018) Privacy and future consent in smart homes as assisted living technologies. In: Hum. Asp. IT Aged Popul. Appl. Health Assist. Entertain. Springer International Publishing, Cham, pp 415–433.

    Google Scholar 

  • Vollmer N (2020) Recital 46 EU general data protection regulation (EU-GDPR). https://www.privacy-regulation.eu/en/recital-46-GDPR.htm.

Download references

Author information

Authors and Affiliations

  1. Department of Information Security and Communication Technology, NTNU i Gjøvik, Gjøvik, Norway

    Luyi Sun & Bian Yang

  2. VP Home and Healthcare, Halodi Robotics AS, Moss, Norway

    Egil Utheim

  3. Zhejiang University, Yuquan Campus, Hangzhou, China

    Hao Luo

Authors
  1. Luyi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bian Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Egil Utheim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luyi Sun .

Editor information

Editors and Affiliations

  1. Bayesian Health, New York, NY, USA

    Pei-Yun Sabrina Hsueh

  2. Heidelberg University Hospital, Institute of Medical Informatics, Heidelberg, Germany

    Thomas Wetter

  3. Center for Biomedical Data Science, Yale University, New Haven, CT, USA

    Xinxin Zhu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Sun, L., Yang, B., Utheim, E., Luo, H. (2022). Privacy Predictive Models for Homecare Patient Sensing. In: Hsueh, PY.S., Wetter, T., Zhu, X. (eds) Personal Health Informatics. Cognitive Informatics in Biomedicine and Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-07696-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-07696-1_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07695-4

  • Online ISBN: 978-3-031-07696-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation