Published on in Vol 2, No 1 (2019): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12153, first published .
Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment Instrument-Home Care: Prospective Observational Study

Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment Instrument-Home Care: Prospective Observational Study

Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment Instrument-Home Care: Prospective Observational Study

Journals

  1. Sixsmith A, Sixsmith J, Fang M, Horst B. AgeTech, Cognitive Health, and Dementia. Synthesis Lectures on Assistive, Rehabilitative, and Health-Preserving Technologies 2020;9(2):i View
  2. Quer G, Arnaout R, Henne M, Arnaout R. Machine Learning and the Future of Cardiovascular Care. Journal of the American College of Cardiology 2021;77(3):300 View
  3. Meyer B, Tulipani L, Gurchiek R, Allen D, Adamowicz L, Larie D, Solomon A, Cheney N, McGinnis R. Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis. IEEE Journal of Biomedical and Health Informatics 2021;25(5):1824 View
  4. Miranda-Duro M, Nieto-Riveiro L, Concheiro-Moscoso P, Groba B, Pousada T, Canosa N, Pereira J. Analysis of Older Adults in Spanish Care Facilities, Risk of Falling and Daily Activity Using Xiaomi Mi Band 2. Sensors 2021;21(10):3341 View
  5. Block V, Pitsch E, Gopal A, Zhao C, Pletcher M, Marcus G, Olgin J, Hollenbach J, Bove R, Cree B, Gelfand J. Identifying falls remotely in people with multiple sclerosis. Journal of Neurology 2022;269(4):1889 View
  6. Lockhart T, Soangra R, Yoon H, Wu T, Frames C, Weaver R, Roberto K. Prediction of fall risk among community-dwelling older adults using a wearable system. Scientific Reports 2021;11(1) View
  7. Blasco J, Pérez-Maletzki J, Díaz-Díaz B, Silvestre-Muñoz A, Martínez-Garrido I, Roig-Casasús S. Fall classification, incidence and circumstances in patients undergoing total knee replacement. Scientific Reports 2022;12(1) View
  8. Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K. Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. Journal of Medical Internet Research 2021;23(11):e26522 View
  9. Kristoffersson A, Du J, Ehn M. Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review. Sensors 2021;21(17):5863 View
  10. Ehn M, Kristoffersson A. Clinical Sensor-Based Fall Risk Assessment at an Orthopedic Clinic: A Case Study of the Staff’s Views on Utility and Effectiveness. Sensors 2023;23(4):1904 View
  11. Huhn S, Axt M, Gunga H, Maggioni M, Munga S, Obor D, Sié A, Boudo V, Bunker A, Sauerborn R, Bärnighausen T, Barteit S. The Impact of Wearable Technologies in Health Research: Scoping Review. JMIR mHealth and uHealth 2022;10(1):e34384 View
  12. Thapa R, Garikipati A, Shokouhi S, Hurtado M, Barnes G, Hoffman J, Calvert J, Katzmann L, Mao Q, Das R. Predicting Falls in Long-term Care Facilities: Machine Learning Study. JMIR Aging 2022;5(2):e35373 View
  13. Orejel Bustos A, Tramontano M, Morone G, Ciancarelli I, Panza G, Minnetti A, Picelli A, Smania N, Iosa M, Vannozzi G. Ambient assisted living systems for falls monitoring at home. Expert Review of Medical Devices 2023;20(10):821 View