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Published on 05.01.15 in Vol 4, No 1 (2015): Jan-Mar

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

Works citing "Do Extreme Values of Daily-Life Gait Characteristics Provide More Information About Fall Risk Than Median Values?"

According to Crossref, the following articles are citing this article (DOI 10.2196/resprot.3931):

(note that this is only a small subset of citations)

  1. Weijer R, Hoozemans M, van Dieën J, Pijnappels M. Self-perceived gait stability modulates the effect of daily life gait quality on prospective falls in older adults. Gait & Posture 2018;62:475
    CrossRef
  2. Almogren A. An automated and intelligent Parkinson disease monitoring system using wearable computing and cloud technology. Cluster Computing 2018;
    CrossRef
  3. Nait Aicha A, Englebienne G, van Schooten K, Pijnappels M, Kröse B. Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry. Sensors 2018;18(5):1654
    CrossRef
  4. Madehkhaksar F, Klenk J, Sczuka K, Gordt K, Melzer I, Schwenk M, Haddad JM. The effects of unexpected mechanical perturbations during treadmill walking on spatiotemporal gait parameters, and the dynamic stability measures by which to quantify postural response. PLOS ONE 2018;13(4):e0195902
    CrossRef
  5. Benson LC, Clermont CA, Bošnjak E, Ferber R. The use of wearable devices for walking and running gait analysis outside of the lab: A systematic review. Gait & Posture 2018;63:124
    CrossRef
  6. Huijben B, van Schooten K, van Dieën J, Pijnappels M. The effect of walking speed on quality of gait in older adults. Gait & Posture 2018;65:112
    CrossRef
  7. Leirós-Rodríguez R, Romo-Pérez V, Arce M, García-Soidán J. Relación entre composición corporal y movimientos producidos durante la marcha en personas mayores. Fisioterapia 2017;39(3):101
    CrossRef
  8. Brodie MA, Coppens MJ, Ejupi A, Gschwind YJ, Annegarn J, Schoene D, Wieching R, Lord SR, Delbaere K. Comparison between clinical gait and daily-life gait assessments of fall risk in older people. Geriatrics & Gerontology International 2017;17(11):2274
    CrossRef
  9. Howcroft J, Kofman J, Lemaire ED. Prospective Fall-Risk Prediction Models for Older Adults Based on Wearable Sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2017;25(10):1812
    CrossRef
  10. Drover D, Howcroft J, Kofman J, Lemaire E. Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features. Sensors 2017;17(6):1321
    CrossRef
  11. van Schooten KS, Pijnappels M, Rispens SM, Elders PJM, Lips P, Daffertshofer A, Beek PJ, van Dieën JH, Glasauer S. Daily-Life Gait Quality as Predictor of Falls in Older People: A 1-Year Prospective Cohort Study. PLOS ONE 2016;11(7):e0158623
    CrossRef
  12. Hamacher D, Hamacher D, Törpel A, Krowicki M, Herold F, Schega L. The reliability of local dynamic stability in walking while texting and performing an arithmetical problem. Gait & Posture 2016;44:200
    CrossRef
  13. Rispens SM, Van Dieën JH, Van Schooten KS, Cofré Lizama LE, Daffertshofer A, Beek PJ, Pijnappels M. Fall-related gait characteristics on the treadmill and in daily life. Journal of NeuroEngineering and Rehabilitation 2016;13(1)
    CrossRef
  14. Pozaic T, Lindemann U, Grebe A, Stork W. Sit-to-Stand Transition Reveals Acute Fall Risk in Activities of Daily Living. IEEE Journal of Translational Engineering in Health and Medicine 2016;4:1
    CrossRef
  15. Howcroft J, Lemaire ED, Kofman J, Gao Z. Wearable-Sensor-Based Classification Models of Faller Status in Older Adults. PLOS ONE 2016;11(4):e0153240
    CrossRef
  16. Pan D, Dhall R, Lieberman A, Petitti DB. A Mobile Cloud-Based Parkinson’s Disease Assessment System for Home-Based Monitoring. JMIR mHealth and uHealth 2015;3(1):e29
    CrossRef