REMOTE HEALTH MONITORING: FALL DETECTION

Mohanad ABDULHAMID

moh1hamid@yahoo.com
Al-Hikma University, Karada Kharidge, Baghdad (Iraq)

Deng PETER


University of Nairobi, P.O. Box 30197-00100, Nairobi (Kenya)

Abstract

Falling is a serious health issue among the elderly population; it can result in critical injuries like hip fractures. Immobilization caused by injury or unconsciousness means that the victim cannot summon help themselves. With elderly who live alone, not being found for hours after a fall is quite common and drastically increases the significance of fall-induced injuries. With an aging Baby Boomer population, the incidence of falls will only rise in the next few decades. The objective of this paper is to design and create a fall detection system. The system consists of a monitoring device that links wirelessly with a laptop. The device is able to accurately distinguish between fall and non-fall.


Keywords:

remote health monitoring, fall detection, design

Gong, S., Wang, Y., Zhang, M., & Wang, C. (2017). Design of remote elderly health monitoring system based on MEMS sensors. In 2017 IEEE International Conference on Information and Automation (ICIA) (pp. 494–498). Macau: IEEE.
DOI: https://doi.org/10.1109/ICInfA.2017.8078958   Google Scholar

Huang, Y., & Newman, K. (2012). Improve quality of care with remote activity and fall detection using ultrasonic sensors. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5854–5857 ). San Diego, CA: IEEE.
  Google Scholar

Malasinghe, L., Ramzan, N., & Dahal, K. (2019). Remote patient monitoring: a comprehensive study. Journal of Ambient Intelligence and Humanized Computing, 10(1), 57–76.
DOI: https://doi.org/10.1007/s12652-017-0598-x   Google Scholar

Saranya, M., Preethi, R., Rupasri, M., & Veena, S. (2018). A survey on health monitoring system by using IOT. International Journal for Research in Applied Science & Engineering Technology, 6(III), 778–782.
DOI: https://doi.org/10.22214/ijraset.2018.3124   Google Scholar

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Published
2020-03-30

Cited by

ABDULHAMID, M., & PETER, D. (2020). REMOTE HEALTH MONITORING: FALL DETECTION. Applied Computer Science, 16(1), 95–102. https://doi.org/10.23743/acs-2020-08

Authors

Mohanad ABDULHAMID 
moh1hamid@yahoo.com
Al-Hikma University, Karada Kharidge, Baghdad Iraq

Authors

Deng PETER 

University of Nairobi, P.O. Box 30197-00100, Nairobi Kenya

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