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].
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Falls. https://www.who.int/news-room/fact-sheets/detail/falls (accessed 6 Feb 2020).
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).
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.
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).
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.
A. Esteva, Skin cancer classification with deep learning. https://cs.stanford.edu/people/esteva/nature/ (accessed 6 Feb 2020).
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.
SR Steinhubl, K-I Kim, T Ajayi, et al. Virtual care for improved global health. Lancet.2018; 391:419.
G Demiris, BK Hensel, M Skubic, et al. Senior residents’ perceived need of and preferences for ‘smart home’ sensor technologies.2008; 24:120–4.
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).
Falls Data | Home and Recreational Safety | CDC Injury Center. 2019. https://www.cdc.gov/HomeandRecreationalSafety/Falls/fallcost.html (accessed 7 Feb 2020).
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).
A Lee, K-W Lee, P Khang, Preventing falls in the geriatric population. Perm J 2013; 17:37–9.
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.
M Kim, X Jiang, K Lauter, S Shams, HEAR: Human Action Recognition via Neural Networks on Homomorphically Encrypted Data, in submission, 2020.
DH Lowenstein, T Bleck, RL Macdonald, It’s time to revise the definition of status epilepticus. Epilepsia 1999; 40:120–2.
BY Su, KC Ho, M Rantz, et al. Radar placement for fall detection: Signature and performance. AIS 2018; 10:21–34.
Homomorphic Encryption Standard, https://homomorphicencryption.org/standard/ HomomorphicEncryption.org, 2017, in this volume, Part 2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
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
DOI: https://doi.org/10.1007/978-3-030-77287-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77286-4
Online ISBN: 978-3-030-77287-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)