Cite

[1] Barry E., Galvin R., Keogh C., Horgan F., Fahey T., Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta-analysis, BMC geriatrics, 14, 1, 2014, doi:10.1186/1471-2318-14-14.10.1186/1471-2318-14-14392423024484314Search in Google Scholar

[2] Beauchet O., Freiberger E., Annweile, C., Kressig R. W., Herrmann F. R., Allali G., Test-retest reliability of stride time variability while dual tasking in healthy and demented adults with frontotemporal degeneration, Journal of neuroengineering and rehabilitation, 8, 1, 2011, doi:10.1186/1743-0003-8-37.10.1186/1743-0003-8-37315672621745370Search in Google Scholar

[3] Bruijn S. M., Meijer O. G., Beek P. J., van Dieën J. H., Assessing the stability of human locomotion: a review of current measures, Journal of the Royal Society Interface,10, 83, 2013, doi:10.1098/rsif.2012.0999.10.1098/rsif.2012.0999364540823516062Search in Google Scholar

[4] Callisaya M. L., Blizzard L., McGinley J. L., Srikanth V. K., Risk of falls in older people during fast-walking - The TASCOG study, Gait and Posture, 36, 3, 2012, 510–515, doi:10.1016/j.gaitpost.2012.05.003.10.1016/j.gaitpost.2012.05.00322682610Search in Google Scholar

[5] Cuaya G., Muñoz-Meléndez A., Morales E. F. A minority class feature selection method, in: C. San Martin, S. W. Kim (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2011, Springer, Berlin, 2011, 417–424.10.1007/978-3-642-25085-9_49Search in Google Scholar

[6] Cuaya G., Muñoz-Meléndez A., Carrera L. N., Morales E. F., Quiñones I., Pérez A. I., Alessi A., A dynamic Bayesian network for estimating the risk of falls from real gait data, Medical and Biological Engineering and Computing, 51, 1–2, 2013, 29–37, doi:10.1007/s11517-012-0960-2.10.1007/s11517-012-0960-223065654Search in Google Scholar

[7] Drover D., Howcroft J., Kofman J., Lemaire E. D., Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features, Sensors, 17, 6, 2017, 13-21, doi:10.3390/s17061321.10.3390/s17061321549229328590432Search in Google Scholar

[8] Gervásio F. M., Santos G. A., Ribeiro D. M., Menezes R., Falls risk detection based on spatiotemporal parameters of three-dimensional gait analysis in healthy adult women from 50 to 70 years old, Fisioterapia e Pesquisa, 23, 4, 2016, 358-364, https://dx.doi.org/10.1590/1809-2950/15661923042016.10.1590/1809-2950/15661923042016Search in Google Scholar

[9] Hamacher D., Schega L., Towards the importance of minimum toe clearance in level ground walking in a healthy elderly population, Gait & Posture, 40, 4, 2014, 727–729, doi:10.1016/j.gaitpost.2014.07.016.10.1016/j.gaitpost.2014.07.01625128155Search in Google Scholar

[10] Hamacher D., Hamacher D., Taylor W. R., Singh N. B., Schega L., Towards clinical application: Repetitive sensor position re-calibration for improved reliability of gait parameters, Gait & Posture, 39, 4, 2014, 1146–1148, doi:10.1016/j.gaitpost.2014.01.020.10.1016/j.gaitpost.2014.01.02024602974Search in Google Scholar

[11] Hassoun M., Fundamentals of Artificial Neural Networks, MIT Press, Cambridge, MA, United States, 1999.Search in Google Scholar

[12] Howcroft J., Kofman J., Lemaire E. D., Review of fall risk assessment in geriatric populations using inertial sensors, Journal of NeuroEngineering and Rehabilitation, 10, 1, 2013, 1–12, doi:Artn 91\nDoi 10.1186/1743-0003-10-91.10.1186/1743-0003-10-91375118423927446Search in Google Scholar

[13] Kabeshova A., Launay C. P., Gromov V. A., Annweiler C., Fantino B., Beauchet O., Artificial Neural Network and Falls in Community-Dwellers: A New Approach to Identify the Risk of Recurrent Falling?, Journal of the American Medical Directors Association,16, 4, 2015, 277–281.10.1016/j.jamda.2014.09.01325444572Search in Google Scholar

[14] Kelsey J. L., Procter-Gray E., Berry S. D., Hannan M. T., Kiel D. P., Lipsitz L. a., Li W., Reevaluating the implications of recurrent falls in older adults: Location changes the inference, Journal of the American Geriatrics Society, 60, 3, 2012, 517–524, doi:10.1111/j.1532-5415.2011.03834.x.10.1111/j.1532-5415.2011.03834.x330297122283236Search in Google Scholar

[15] Lai D. T. H., Begg R. K., Palaniswami M., SVM Models for Diagnosing Balance Problems Using Statistical Features of the Mtc Signal, International Journal of Computational Intelligence and Applications, 7, 3, 2008, 317–331, doi:10.1142/S1469026808002314.10.1142/S1469026808002314Search in Google Scholar

[16] Lindsey C., Brownbill R. A., Bohannon R. A., Ilich J. Z., Association of physical performance measures with bone mineral density in postmenopausal women, Archives of Physical Medicine and Rehabilitation, 86, 6, 2005, 1102–1107, doi:10.1016/j.apmr.2004.09.028.10.1016/j.apmr.2004.09.02815954047Search in Google Scholar

[17] Livingston F., Implementation of Breiman’s Random Forest Machine Learning Algorithm, Machine Learning, 2005, 1-13.Search in Google Scholar

[18] McGough E. L., Logsdon R. G., Kelly V. E., Teri L., Functional Mobility Limitations and Falls in Assisted Living Residents With Dementia, Journal of Geriatric Physical Therapy, 36, 1, 2012, doi:10.1519/JPT.0b013e318268de7f.10.1519/JPT.0b013e318268de7f22976811Search in Google Scholar

[19] Nait A., Englebienne G, van Schooten K. S., Pijnappels M., Kröse B., Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry, Sensors, 18, 2018, 1-14.10.3390/s18051654598119929786659Search in Google Scholar

[20] Perera S., Mody S. H., Woodman R. C., Studenski S. A., Meaningful change and responsiveness in common physical performance measures in older adults, Journal of the American Geriatrics Society, 54, 5, 2006, 743–749, doi:10.1111/j.1532-5415.2006.00701.x.10.1111/j.1532-5415.2006.00701.x16696738Search in Google Scholar

[21] Pfortmueller C., Reducing fall risk in the elderly: risk factors and fall prevention. Minerva Med, 105, 2014, 275–281.Search in Google Scholar

[22] Phelan E. A., Mahoney J. E., Voit J. C., Stevens J. A., Assessment and Management of Fall Risk in Primary Care Settings, Medical Clinics of North America, 99, 2, 2015, 281–293, doi:10.1016/j.mcna.2014.11.004.10.1016/j.mcna.2014.11.004470766325700584Search in Google Scholar

[23] Rong-En F., Pai-Hsuen C., Chih-Jen L., Working Set Selection Using Second Order Information for Training Support Vector Machines, Journal Machine Learning Research, 6, 2005, 1889–1918.Search in Google Scholar

[24] Smith M. I., de Lusignan S., Mullett D., Correa A., Tickner J., Jones S., Predicting Falls and When to Intervene in Older People: A Multilevel Logistical Regression Model and Cost Analysis, PloS one, 11, 7, 2016, doi:10.1371/journal.pone.0159365.10.1371/journal.pone.0159365495775627448280Search in Google Scholar

[25] Webster K. E., Wittwer J. E., Feller, J. A., Validity of the GAITRite?? walkway system for the measurement of averaged and individual step parameters of gait, Gait & Posture, 22, 4, 2005, 317–321, doi:10.1016/j.gaitpost.2004.10.005.10.1016/j.gaitpost.2004.10.00516274913Search in Google Scholar

eISSN:
2300-3405
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Artificial Intelligence, Software Development