Advertisement

Design and Implementation of Bed-Exit Alarm System for Preventing Elderly Falling

  • Ching-Ta WuEmail author
  • Chien-Hsu Chen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 957)

Abstract

In recent years, with the aging of the population, the increase in the elderly population has brought a greater burden on medical institutions. Therefore, how to reduce the incidence of hospitalization through medical technology will become an important issue for hospitals. In many hospitalization accidents, the greatest threat to the elderly due to falls. Besides immediate dangers, the sequelae will also increase the burden on caregivers and health care. In order to reduce the second-degree injury caused by falls, hospitals or nursing homes often use the bed-exit alarm system to actively inform the nurse or caregiver to help when the patient gets out of bed. However, the false alarm of related products on the market are too frequent, which not only causes the caregivers to be exhausted, but also delay critical rescue opportunities.

Based on the above issues, this study will propose two new types of bed-exit alarm systems, one of which is based on the nurse’s clinical care experience, setting up multiple sensing such as infrared, ultrasonic and triaxial accelerometers on the route that patients pass by most often. Another set of bed-exit alarm system uses the infrared array sensor mounted on the headboard of the bed to capture the patient’s thermal imaging, and then uses the support vector machine to analyze the position of the patient on the bed to determine whether the behavior is about to leave the bed. Unlike traditional image processing, this method eliminates the need for edge detection and motion tracking and is immune to ambient light sources.

Keywords

Bed-exit alarm system Elderly Falling 

References

  1. Tinetti, M.E., Inouye, S.K., Gill, T.M., Doucette, J.T.: Shared risk. Factors for falls, incontinence, and functional dependence: unifying the approach to geriatric syndromes. JAMA 273(17), 1348–1353 (1995)CrossRefGoogle Scholar
  2. Inouye, S.K.: Prevention of delirium in hospitalized older patients: risk factors and targeted intervention strategies. Ann. Med. 32(4), 257–263 (2000)MathSciNetCrossRefGoogle Scholar
  3. Bischoff-Ferrari, H.A., Dawson-Hughes, B., Staehelin, H.B., Orav, J.E., Stuck, A.E., Theiler, R., Wong, J.B., Egli, A., Kiel, D.P., Henschkowski, J.: Fall prevention with supplemental and active forms of vitamin D: a meta-analysis of randomised controlled trials. BMJ 339, b3692 (2009)CrossRefGoogle Scholar
  4. Verma, S.K., Willetts, J.L., Corns, H.L., Marucci-Wellman, H.R., Lombardi, D.A., Courtney, T.K.: Falls and fall-related injuries among community-dwelling adults in the United States. PLoS One 11(3), e0150939 (2016)CrossRefGoogle Scholar
  5. Bekkers, E., Dockx, K., Heremans, E., Verschueren, S., Mirelman, A., Hausdorff, J.M., Nieuwboer, A.: The effect of treadmill training with and without virtual reality on postural control and gait in elderly fallers (2015) Google Scholar
  6. Woollacott, M.H., Shumway-Cook, A.: Changes in posture control across the life span—a systems approach. Phys. Ther. 70(12), 799–807 (1990)CrossRefGoogle Scholar
  7. Kosse, N.M., Brands, K., Bauer, J.M., Hortobágyi, T., Lamoth, C.J.: Sensor technologies aiming at fall prevention in institutionalized old adults: a synthesis of current knowledge. Int. J. Med. Inform. 82(9), 743–752 (2013)CrossRefGoogle Scholar
  8. Berlie, H.D., Garwood, C.L.: Diabetes medications related to an increased risk of falls and fall-related morbidity in the elderly. Ann. Pharmacother. 44(4), 712–717 (2010)CrossRefGoogle Scholar
  9. Guo, Z., Wills, P., Viitanen, M., Fastbom, J., Winblad, B.: Cognitive impairment, drug use, and the risk of hip fracture in persons over 75 years old: a community-based prospective study. Am. J. Epidemiol. 148(9), 887–892 (1998)CrossRefGoogle Scholar
  10. Ray, W.A., Thapa, P.B., Gideon, P.: Benzodiazepines and the risk of falls in nursing home residents. J. Am. Geriatr. Soc. 48(6), 682–685 (2000)CrossRefGoogle Scholar
  11. Woolcott, J.C., Richardson, K.J., Wiens, M.O., Patel, B., Marin, J., Khan, K.M., Marra, C.A.: Meta-analysis of the impact of 9 medication classes on falls in elderly persons. Arch. Intern. Med. 169(21), 1952–1960 (2009)CrossRefGoogle Scholar
  12. Robbins, S., Gouw, G.J., McClaran, J.: Shoe sole thickness and hardness influence balance in older men. J. Am. Geriatr. Soc. 40(11), 1089–1094 (1992)CrossRefGoogle Scholar
  13. Tzeng, H.M., Yin, C.Y.: Most and least helpful aspects of fall prevention education to prevent injurious falls: a qualitative study on nurses’ perspectives. J. Clin. Nurs. 23(17–18), 2676–2680 (2014)CrossRefGoogle Scholar
  14. Liu, H., Huang, J., Lu, C., Lan, Z., Wang, Q.: Indoor monitoring system for elderly based on ZigBee network. In: 2016 International Symposium on Paper Presented at the Micro-NanoMechatronics and Human Science (MHS) (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tainan CityTaiwan (R.O.C.)

Personalised recommendations