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cStick: A Calm Stick for Fall Prediction, Detection and Control in the IoMT Framework

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Internet of Things. Technology and Applications (IFIPIoT 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 641))

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Abstract

Falls are constant threats to older adults and can minimize their ability to live independently. To help mitigate the occurrences and effects of such unfortunate accidents, it is imperative to find an accurate, reliable, robust and convenient solution to make life easier for elder adults who may have visual or hearing impairments. In order to reduce such occurrences, a calm stick, cStick is proposed. cStick is an IoT (Internet of Things) enabled system which has a capability to predict falls before their occurrence, to warn the user that there may be an incident of fall, to detect falls and also provide control remedies to reduce their impact. cStick monitors the location of the user, the physiological changes that occur when a person is about to fall and also monitors the surroundings the user is in when having an incident of fall. Based on the changes in the monitored parameters, the decision of fall i.e., a prediction, warning or detection of fall is made with an accuracy of approximately 95%. Control mechanisms to reduce the impact of the fall along with connection capabilities to the help unit are provided with the cStick system.

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References

  1. Alshammari, S.A., et al.: Falls among elderly and its relation with their health problems and surrounding environmental factors in Riyadh. J. Family Community Med. 25(1), 29–34 (2018)

    MathSciNet  Google Scholar 

  2. Apple: Apple Watch. https://support.apple.com/en-us/HT208944

  3. Avin, K.G., et al.: Management of falls in community-dwelling older adults: clinical guidance statement from the academy of geriatric physical therapy of the american physical therapy association. Phys. Therapy 95(6), 815–834 (2015)

    Article  Google Scholar 

  4. Bergen, G., Stevens, M.R., Burns, E.R.: Falls and fall injuries among adults aged 65 years - United States, 2014. MMWR Morb. Mortal Wkly. Rep. 65(37), 993–998 (2016)

    Article  Google Scholar 

  5. Boss, G.R., Seegmiller, J.E.: Age-related physiological changes and their clinical significance. W. J. Med. (1981)

    Google Scholar 

  6. Christou, D.D., Seals, D.R.: Decreased maximal heart rate with aging is related to reduced beta-adrenergic responsiveness but is largely explained by a reduction in intrinsic heart rate. J. Appl. Physiol. 105(1), 24–29 (2008). https://doi.org/10.1152/japplphysiol.90401.2008

    Article  Google Scholar 

  7. Dionyssiotis, Y.: Analyzing the problem of falls among older people. Int. J. Gen. Med. 5, 805–813 (2012)

    Article  Google Scholar 

  8. Edelman, M.M.S., Ficorelli, C.T.: Keeping older adults safe at home. Nursing 42(1), 65–66 (2012)

    Article  Google Scholar 

  9. Engel, W., Ding, W.: Reliable and practical fall prediction using artificial neural network. In: 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). pp. 1867–1871 (2017). https://doi.org/10.1109/FSKD.2017.8393052

  10. Gregg, E.W., et al.: Diabetes and incidence of functional disability in older women. Diabetes Care 25(1), 61–7 (2002)

    Article  Google Scholar 

  11. Hamdan, S., Ayyash, M., Almajali, S.: Edge-Computing architectures for internet of things applications: a survey. Sensors (Basel) 20(22), 6411–6463 (2020)

    Article  Google Scholar 

  12. Han, H., Ma, X., Oyama, K.: Towards detecting and predicting fall events in elderly care using bidirectional electromyographic sensor network. In: IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pp. 1–6 (2016). https://doi.org/10.1109/ICIS.2016.7550897

  13. Hartholt, K.A., Lee, R., Burns, E.R., van Beeck, E.F.: Mortality from falls among US adults aged 75 years or older, 2000–2016. JAMA 321(21), 2131–2133 (2019)

    Article  Google Scholar 

  14. Hjalmarsen, A., Hykkerud, D.L.: Severe nocturnal hypoxaemia in geriatric inpatients. Age Ageing 37(5), 526–529 (2008)

    Article  Google Scholar 

  15. Home, V.: Vayyar Walabot (2020), https://vayyar.com/

  16. Hope, H.: Hip-Hope\(^{{\rm TM}}\) smart wearable hip protector using cutting-edge technology and design (2020), https://www.hip-hope.com/

  17. Immonen, M., et al.: Association between Chronic Diseases and falls among a Sample of Older People in Finland. BMC Geriatr. 20(225), 1–12 (2020)

    Google Scholar 

  18. Lin, F.R., Ferrucci., L.: Hearing loss and falls among older adults in the United States. Arch. Int. Med. 172(4), 369–371 (2012)

    Google Scholar 

  19. Mohanty, S.P., Choppali, U., Kougianos, E.: Everything you wanted to know about smart cities: the internet of things is the backbone. IEEE Consum. Electr. Mag. 5(3), 60–70 (2016). https://doi.org/10.1109/MCE.2016.2556879

    Article  Google Scholar 

  20. Nižetić, S., Šolić, P., López Artaza, D., Patrono, L.: Internet of Things (IoT): opportunities, issues and challenges towards a smart and sustainable future. J. Clean. Prod. 274(122877), 1–33 (2020)

    Google Scholar 

  21. O’Loughlin, J.L., Robitaille, Y., Boivin, J.F., Suissa, S.: Incidence of and risk factors for falls and injurious falls among the community-dwelling elderly. Am. J. Epidemiol. 137(3), 342–354 (1993)

    Article  Google Scholar 

  22. Owlytics: Owlytics Healthcare. (2020). https://www.owlytics.com/

  23. Paulo, H., Pelicionia, S.R.L.: COVID-19 will severely impact older people’s lives, and in many more ways than you think! Braz J. Phys. Ther. 24(4), 293–294 (2020)

    Article  Google Scholar 

  24. Pohl, J., Cochrane, B.B., Schepp, K.G., Woods, N.F.: Falls and the social isolation of older adults in the national health and aging trends study. Innov. Aging 1(1), 268–269 (2017)

    Article  Google Scholar 

  25. Pongthanisorn, G., Viriyavit, W., Prakayapan, T., Deepaisam, S., Somlertlamvanich, V.: ECS: elderly care system for fall and bedsore prevention using non-constraint sensor. In: International Electronics Symposium (IES), pp. 340–344 (2020). https://doi.org/10.1109/IES50839.2020.9231781

  26. Rachakonda, L., Sharma, A., Mohanty, S.P., Kougianos, E.: Good-eye: a combined computer-vision and physiological-sensor based device for full-proof prediction and detection of fall of adults. 574, 273–288 (2019)

    Google Scholar 

  27. Rachakonda, L., Bapatla, A.K., Mohanty, S.P., Kougianos, E.: SaYoPillow: blockchain-integrated privacy-assured IoMT framework for stress management considering sleeping habits. IEEE Trans. Consum. Electr. 67(1), 20–29 (2021). https://doi.org/10.1109/TCE.2020.3043683

    Article  Google Scholar 

  28. Razmara, J., Zaboli, M.H., Hassankhani, H.: Elderly fall risk prediction based on a physiological profile approach using artificial neural networks. Health Inform. J. 24, 410–418 (2018)

    Article  Google Scholar 

  29. Steimer, T.: The biology of fear- and anxiety-related behaviors. Dialog. Clin. Neurosci. 4(3), 231–249 (2002)

    Article  Google Scholar 

  30. Stevens, J.A., et al.: Gender differences in seeking care for falls in the aged medicare population. Am. J. Prev. Med. 43(1), 59–62 (2012)

    Article  Google Scholar 

  31. Taylor, C.A., Bell, J.M., Breiding, M.J., Xu, L.: Traumatic brain injury-related emergency department visits, hospitalizations, and deaths - United States, 2007 and 2013. MMWR Surveill. Summ. 66(9), 1–16 (2017)

    Article  Google Scholar 

  32. Özdemir, A.T., Barshan, B.: Detecting falls with wearable sensors using machine learning techniques. Sensors 14(6), 10691–10708 (2014). https://doi.org/10.3390/s140610691, https://www.mdpi.com/1424-8220/14/6/10691

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of North Carolina Wilmington, Wilmington, USA

    Laavanya Rachakonda

  2. Department of Computer Science and Engineering, University of North Texas, Denton, USA

    Saraju P. Mohanty

  3. Department of Electrical Engineering, University of North Texas, Denton, USA

    Elias Kougianos

Authors
  1. Laavanya Rachakonda
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  2. Saraju P. Mohanty
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  3. Elias Kougianos
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Corresponding author

Correspondence to Saraju P. Mohanty .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Monte Caparica, Portugal

    Luis M. Camarinha-Matos

  2. University of Twente, Enschede, The Netherlands

    Geert Heijenk

  3. University of South Florida, Tampa, FL, USA

    Srinivas Katkoori

  4. De Nederlandsche Bank, Amsterdam, The Netherlands

    Leon Strous

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Rachakonda, L., Mohanty, S.P., Kougianos, E. (2022). cStick: A Calm Stick for Fall Prediction, Detection and Control in the IoMT Framework. In: Camarinha-Matos, L.M., Heijenk, G., Katkoori, S., Strous, L. (eds) Internet of Things. Technology and Applications. IFIPIoT 2021. IFIP Advances in Information and Communication Technology, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-030-96466-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-96466-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96465-8

  • Online ISBN: 978-3-030-96466-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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