This paper is in the following e-collection/theme issue:

Artificial Intelligence (1676) Assisted Living for the Elderly and Nursing Home Care (185) Dementia and Cognitive Decline (573) Telehealth and Telemonitoring (1853) Falls and Emergency Detection with Sensors and other Technologies (41) Falls Prevention in the Elderly (81) Memory (47)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17551, first published .
Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study

Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study

Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study

Authors of this article:

Eleonore Bayen1, 2 Author Orcid Image ;   Shirley Nickels3 Author Orcid Image ;   Glen Xiong4 Author Orcid Image ;   Julien Jacquemot3 Author Orcid Image ;   Raghav Subramaniam3 Author Orcid Image ;   Pulkit Agrawal3, 5 Author Orcid Image ;   Raheema Hemraj3 Author Orcid Image ;   Alexandre Bayen6 Author Orcid Image ;   Bruce L Miller2 Author Orcid Image ;   George Netscher3 Author Orcid Image
Article Authors Cited by (11) Tweetations (10) Metrics

Journals

  1. O'Connor S. Artificial Intelligence for Older Adult Health: Opportunities for Advancing Gerontological Nursing Practice. Journal of Gerontological Nursing 2022;48(12):3 View
  2. Fakhouri F, Fakhouri A, Ijaz M, Alotaibi M, Almalki A. Design of an After-Fall-Assistive Device for Elderly Patients by Finite Element Methods. Journal of Disability Research 2023;2(4) View
  3. Lear R, Ellis S, Ollivierre-Harris T, Long S, Mayer E. Video Recording Patients for Direct Care Purposes: Systematic Review and Narrative Synthesis of International Empirical Studies and UK Professional Guidance. Journal of Medical Internet Research 2023;25:e46478 View
  4. Eost-Telling C, Yang Y, Norman G, Hall A, Hanratty B, Knapp M, Robinson L, Todd C. Digital technologies to prevent falls in people living with dementia or mild cognitive impairment: a rapid systematic overview of systematic reviews. Age and Ageing 2024;53(1) View
  5. Gallistl V, von Laufenberg R, Lehner K. Vulnerability Assemblages: Situating Vulnerability in the Political Economy of Artificial Intelligence. Socius: Sociological Research for a Dynamic World 2024;10 View
  6. Zimmerman S, Xiong G, Netscher G. Letter to the Editor in Response to “Meta-Analysis of Fall Risk in Older Adults with Alzheimer's Disease”. Journal of the American Medical Directors Association 2024;25(11):105221 View
  7. Zimmerman S, Sloane P, Preisser J, Ward K, Wretman C, Davis S, Figueiro M. Feasibility of a Novel Lighting System to Reduce Nighttime Falls in Assisted Living Residents With Dementia. Journal of the American Medical Directors Association 2024;25(10):105227 View
  8. Huang H, Chang M, Chen P, Lin C, Sung P, Chen C, Fan S. Exploring factors affecting the acceptance of fall detection technology among older adults and their families: a content analysis. BMC Geriatrics 2024;24(1) View
  9. Traverso A, Bayram A, Rossettini G, Chiappinotto S, Galazzi A, Palese A. Investigating the biomechanics of falls in older adults in long-term care using a video camera: a scoping review. BMC Geriatrics 2024;24(1) View
  10. 松田 直. 総合診療科におけるAI研究. Nihon Ika Daigaku Igakkai Zasshi 2025;21(1):11 View
  11. Steijger D, Christie H, Aarts S, IJselsteijn W, Verbeek H, de Vugt M. Use of artificial intelligence to support quality of life of people with dementia: A scoping review. Ageing Research Reviews 2025;108:102741 View