Skip to main content

Trusted Monitoring Service (TMS)

  • Chapter
  • First Online:
Protecting Privacy through Homomorphic Encryption

Abstract

In healthcare, timely monitoring of patients is a big problem, especially those who live alone in their own home or nursing home. There are many situations where people fall but no help is available, which leads to severe conditions and even mortality. Falls are the second leading cause of accidental or unintentional injury deaths worldwide, each year an estimated 646,000 individuals die from falls globally of which over 80%, are in low- and middle- income countries [1]. In the US, the age-adjusted rate of fall deaths is 62 deaths per 100,000 older adults and this rate is increasing [2]. Fall death rates among adults aged 65 and older have increased more than 30% from 2007 to 2016 [3]. Among older people in the U.S. (age 65+) there are approximately 750,000 falls per year requiring hospitalization due to either bone fracturing (approx. 480,000 cases) or hip fracturing (approx. 270,000 cases) [4].

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.99
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

References

  1. Falls. https://www.who.int/news-room/fact-sheets/detail/falls (accessed 6 Feb 2020).

  2. Deaths from Falls | Home and Recreational Safety | CDC Injury Center. 2019. https://www.cdc.gov/homeandrecreationalsafety/falls/fallcost/deaths-from-falls.html (accessed 6 Feb 2020).

  3. E. Burns, R. Kakara, Deaths from Falls Among Persons Aged ≥ 65 Years-United States, 2007-2016. MMWR Morb Mortal Wkly Rep 2018; 67:509–14.

    Google Scholar 

  4. TJ Petelenz, SC Peterson, SC Jacobsen, Elderly fall monitoring method and device, US Patent, 2002. https://patentimages.storage.googleapis.com/c5/7a/c0/8431d535a77e29/US6433690.pdf(accessed 7 Feb 2020).

  5. MA Badgeley, JR Zech, L Oakden-Rayner, et al. Deep learning predicts hip fracture usingconfounding patient and healthcare variables. NPJ Digit Med 2019; 2:31.

    Google Scholar 

  6. A. Esteva, Skin cancer classification with deep learning. https://cs.stanford.edu/people/esteva/nature/ (accessed 6 Feb 2020).

  7. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic Assessment of DeepLearning Algorithms for Detection of Lymph Node Metastases in Women With BreastCancer. JAMA 2017; 318:2199–210.

    Google Scholar 

  8. SR Steinhubl, K-I Kim, T Ajayi, et al. Virtual care for improved global health. Lancet.2018; 391:419.

    Google Scholar 

  9. G Demiris, BK Hensel, M Skubic, et al. Senior residents’ perceived need of and preferences for ‘smart home’ sensor technologies.2008; 24:120–4.

    Google Scholar 

  10. C Bradford, 7 Most Infamous Cloud Security Breaches – StorageCraft. StorageCraft Technology Corporation 2017. https://blog.storagecraft.com/7-infamous-cloud-security-breaches/ (accessed 7 Feb 2020).

  11. Falls Data | Home and Recreational Safety | CDC Injury Center. 2019. https://www.cdc.gov/HomeandRecreationalSafety/Falls/fallcost.html (accessed 7 Feb 2020).

  12. Fall Detection Systems Market Analysis – Global Industry Size, Share, Growth Opportunity, Trends and Forecast 2026. MarketWatch. https://www.marketwatch.com/press-release/fall-detection-systems-market-analysis-global-industry-size-share-growth-opportunity-trends-and-forecast-2026-2019-04-05 (accessed 7 Feb 2020).

  13. A Lee, K-W Lee, P Khang, Preventing falls in the geriatric population. Perm J 2013; 17:37–9.

    Google Scholar 

  14. C Roberts, How to choose a medical alert system. https://www.consumerreports.org/medical-alert-systems/how-to-choose-a-medical-alert-system/ Published Online First: 2018.

  15. M Kim, X Jiang, K Lauter, S Shams, HEAR: Human Action Recognition via Neural Networks on Homomorphically Encrypted Data, in submission, 2020.

    Google Scholar 

  16. DH Lowenstein, T Bleck, RL Macdonald, It’s time to revise the definition of status epilepticus. Epilepsia 1999; 40:120–2.

    Google Scholar 

  17. BY Su, KC Ho, M Rantz, et al. Radar placement for fall detection: Signature and performance. AIS 2018; 10:21–34.

    Google Scholar 

  18. Homomorphic Encryption Standard, https://homomorphicencryption.org/standard/ HomomorphicEncryption.org, 2017, in this volume, Part 2.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tim Scott .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 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

Jiang, X., Kim, M., Lauter, K., Scott, T., Shams, S. (2021). Trusted Monitoring Service (TMS). In: Lauter, K., Dai, W., Laine, K. (eds) Protecting Privacy through Homomorphic Encryption. Springer, Cham. https://doi.org/10.1007/978-3-030-77287-1_5

Download citation

Publish with us

Policies and ethics