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Robots in Nursing Homes: Helping Nurses Detect and Prevent Falls
Robots in Nursing Homes: Helping Nurses Detect and Prevent Falls
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| DOI | 10.20900/agmr20250001 |
| 刊名 |
AGMR
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| 年，卷(期) | 2025, 7(1) |
| 作者 |
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| 作者单位 |
Vigorous Mind, Inc., Natick, MA 01760, USA ; |
| 摘要 |
Falls are a leading cause of morbidity and mortality in older adults, especially among nursing home residents. Falls occur more commonly among older adults with dementia than among those without dementia. Moreover, half of nursing home residents have moderate to severe cognitive impairment. While less than 5% of older adults live in nursing homes, they account for 20% of deaths from falls in this age group. In addition, 78% of older adults who fall need help in getting up from the floor. The consequences of falling, such as prolonged lying on the floor, can produce severe and prolonged health effects. The acute shortage of staff in nursing homes, especially during evening, night and weekend shifts, can delay the detection and response to falls. There are various systems designed to detect falls and alert staff, including those utilizing wearable devices, ambience sensors and cameras (vision) as well as fusion systems. They each have their advantages and drawbacks. In this NIA-funded SBIR grant, we are developing and testing the feasibility of a fall detection and prevention system that addresses the drawbacks of previous systems. We anchor our approach on the deployment of an autonomously navigating robot equipped with a mounted infrared camera and machine learning software designed to detect the risk of falls and falls themselves. The robot will patrol resident rooms during evening and night shifts and alert the staff, allowing them to evaluate the fall risk or fall alert presented by the robot video camera and determine whether indeed a resident has fallen or is at risk of falling, and take appropriate action.
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| Abstract |
Falls are a leading cause of morbidity and mortality in older adults, especially among nursing home residents. Falls occur more commonly among older adults with dementia than among those without dementia. Moreover, half of nursing home residents have moderate to severe cognitive impairment. While less than 5% of older adults live in nursing homes, they account for 20% of deaths from falls in this age group. In addition, 78% of older adults who fall need help in getting up from the floor. The consequences of falling, such as prolonged lying on the floor, can produce severe and prolonged health effects. The acute shortage of staff in nursing homes, especially during evening, night and weekend shifts, can delay the detection and response to falls. There are various systems designed to detect falls and alert staff, including those utilizing wearable devices, ambience sensors and cameras (vision) as well as fusion systems. They each have their advantages and drawbacks. In this NIA-funded SBIR grant, we are developing and testing the feasibility of a fall detection and prevention system that addresses the drawbacks of previous systems. We anchor our approach on the deployment of an autonomously navigating robot equipped with a mounted infrared camera and machine learning software designed to detect the risk of falls and falls themselves. The robot will patrol resident rooms during evening and night shifts and alert the staff, allowing them to evaluate the fall risk or fall alert presented by the robot video camera and determine whether indeed a resident has fallen or is at risk of falling, and take appropriate action.
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| 关键词 |
fall detection; fall prevention; robot; nursing homes; long lying
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| KeyWord |
fall detection; fall prevention; robot; nursing homes; long lying
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| 基金项目 | |
| 页码 | - |
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1.Moreland B, Kakara R, Henry A. Trends in Nonfatal Falls and Fall-Related Injuries Among Adults Aged ≥65 Years—United States, 2012–2018. Morb Mortal Wkly Rep. 2020;69(27):875-81.
2.Burns E, Kakara R. Deaths from falls among persons aged ≥65 years—United States, 2007–2016. Morb Mortal Wkly Rep. 2018;67:509-14.
3.Rapp K, Becker C, Cameron ID, Konig HH, Buchele G. Epidemiology of Falls in Residential Aged Care: Analysis of More Than 70,000 Falls From Residents of Bavarian Nursing Homes. J Am Med Dir Assoc. 2012;13(2):187.e1-6.
4.Rubenstein LZ, Josephson KR, Robbins AS. Falls in the nursing home. Ann Intern Med. 1994;121(6):442-51.
5.Centers for Medicare & Medicaid Services (CMS). Nursing Home Data Compendium 2015. Available from: https://www.hhs.gov/guidance/document/nursing-home-data-compendium-2015-edition. Accessed on 25 Dec 2024.
6.Taylor ME, Lord SR, Delbaere K, Wen W, Jiang J, Brodaty H, et al. White matter hyperintensities are associated with falls in older people with dementia. Brain Imaging Behav. 2019;13:1265-72.
7.Marcum ZA, Dai Z, Tan ECK. Prevalence and risk factors for falls and fall-related injuries in the 2018 National Post-acute and Long-term Care Study. J Am Geriatr Soc. 2022;71(1):276.
8.Scheffer AC, Schuurmans MJ, van Dijk N, van der Hooft T, de Rooij SE. Systematic Review, Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing. 2008;37:19-24.
9.Lester P, Haq M, Vadnerkar A, Feuerman M. Falls in the nursing home setting: does time matter? J Am Med Dir Assoc. 2008;9(9):684-6.
10.Kubitza J, Schneider IT, Reuschenbach B. Concept of the Term long lie: a scoping review. Eur Rev Aging Phys A. 2023;20(1):16.
11.Bloch F. Critical Falls: Why Remaining on the Ground After a Fall can be Dangerous, Whatever the Fall. J Am Ger Soc. 2012;60(7):1375-6.
12.Heiks C, Sabine N. Long Term Care and Skilled Nursing Facilities. Dela J Pub Health. 2022;8(5):144-9.
13.American Health Care Association (AHCA). State of the Nursing Home Sector. Available from: https://www.ahcancal.org/News-and-Communications/Fact-Sheets/FactSheets/AHCA%20State%20of%20the%20Sector%202024.pdf. Accessed on 25 Dec 2024.
14.Mubashir M, Shao L, Seed L. A survey of fall detection: Principles and approaches. Neurocomputing. 2013;100:144-52.
15.Ren L, Peng Y. Research of Fall Detection and Fall Prevention Technologies: A Systematic Review. IEEE Access. 2019;7:77702-22.
16.Fernández-Bermejo Ruiz J, Dorado Chaparro J, Santofimia Romero MJ, Villanueva Molina FJ, García XT, Peño CB, et al. Bedtime Monitoring for Fall Detection and Prevention in Older Adults. Int J Environ Res Public Health. 2022;19(12):7139.
17.Mileski M, Brooks M, Topinka JB, Hamilton G, Land C, Mitchell T, et al. Alarming and/or Alerting Device Effectiveness in Reducing Falls in Long-Term Care (LTC) Facilities? A Systematic Review. Healthcare. 2019;7(1):51.
18.Shota O, Drummond A, Kendrick D, Grainge MJ, Vass C, Sach T, et al. REFINE (Reducing Falls in In-PatieNt Elderly) using bed and bedside chair pressure sensors linked to radio-pagers in acute hospital care: a randomized controlled trial. Age Aging. 2014;43:247-53.
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