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Video-Surveillance System for Fall Detection in the Elderly

  • Conference paper
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HCI International 2019 - Posters (HCII 2019)

Abstract

Recently, the number of households comprising only elderly people(60 years old or older) has increased because of the falling birth rate and the aging population. According to a recent Japanese Statistics Bureau report, the total population was estimated to be 126.59 million among which 35.22 million people were elderly. Furthermore, the Ministry of Health, Labor, and Welfare predicted a shortage of approximately 380,000 nursing care staff in Japan by 2025 [1], which is the year in which the baby-boomer generation is expected to become more than 75 years old. As the number of users of nursing care services increases, 2.53 million nursing staff will become necessary by 2025; however, it is expected that only 2.15 million staff will be present based on the current rate of increase. According to the official release of the sufficiency rate associated with the number of nursing care staff actually required to serve the number of people who requires them, which increase with the aging population, there will be a shortage of care workers of approximately 200,000 in 2020 and of approximately 380,000 in 2025. Therefore, we have developed a video-surveillance system capable of detecting an elderly person falling in the absence of care workers.

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References

  1. Ministry of Health, Labor and Welfare Information. https://www.mhlw.go.jp/seisakunitsuite/bunnya/hukushi_kaigo/kaigo_koureisha/chiiki-houkatsu/dl/link1-1.pdf. Accessed 10 Feb 2019

  2. Web site information. http://poly.hatenablog.com/entry/2014/01/06/063012. Accessed 4 Feb 2019

  3. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  4. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  5. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF, pp. 2564–2571 (2011)

    Google Scholar 

  6. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection (2005)

    Google Scholar 

  7. Alcantarilla, P.F., Nuevo, J., Bartoli, A.: Fast explicit diffusion for accelerated features in nonlinear scale spaces (2012)

    Google Scholar 

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Correspondence to Koudai Yano .

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Yano, K. et al. (2019). Video-Surveillance System for Fall Detection in the Elderly. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-23528-4_45

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  • DOI: https://doi.org/10.1007/978-3-030-23528-4_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23527-7

  • Online ISBN: 978-3-030-23528-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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