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Items: 1 to 20 of 127

1.

Fall classification by machine learning using mobile phones.

Albert MV, Kording K, Herrmann M, Jayaraman A.

PLoS One. 2012;7(5):e36556. doi: 10.1371/journal.pone.0036556. Epub 2012 May 7.

2.

A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.

Aziz O, Musngi M, Park EJ, Mori G, Robinovitch SN.

Med Biol Eng Comput. 2017 Jan;55(1):45-55. doi: 10.1007/s11517-016-1504-y. Epub 2016 Apr 22.

PMID:
27106749
3.

Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets.

Aziz O, Klenk J, Schwickert L, Chiari L, Becker C, Park EJ, Mori G, Robinovitch SN.

PLoS One. 2017 Jul 5;12(7):e0180318. doi: 10.1371/journal.pone.0180318. eCollection 2017.

4.

Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications.

Shawen N, Lonini L, Mummidisetty CK, Shparii I, Albert MV, Kording K, Jayaraman A.

JMIR Mhealth Uhealth. 2017 Oct 11;5(10):e151. doi: 10.2196/mhealth.8201.

5.

Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers.

Aziz O, Park EJ, Mori G, Robinovitch SN.

Gait Posture. 2014;39(1):506-12. doi: 10.1016/j.gaitpost.2013.08.034. Epub 2013 Sep 23.

PMID:
24148648
6.

The effect of window size and lead time on pre-impact fall detection accuracy using support vector machine analysis of waist mounted inertial sensor data.

Aziz O, Russell CM, Park EJ, Robinovitch SN.

Conf Proc IEEE Eng Med Biol Soc. 2014;2014:30-3. doi: 10.1109/EMBC.2014.6943521.

PMID:
25569889
7.

Accelerometer and Camera-Based Strategy for Improved Human Fall Detection.

Zerrouki N, Harrou F, Sun Y, Houacine A.

J Med Syst. 2016 Dec;40(12):284. Epub 2016 Oct 29.

PMID:
27796842
8.

Detecting falls as novelties in acceleration patterns acquired with smartphones.

Medrano C, Igual R, Plaza I, Castro M.

PLoS One. 2014 Apr 15;9(4):e94811. doi: 10.1371/journal.pone.0094811. eCollection 2014.

9.

Detecting falls with wearable sensors using machine learning techniques.

Özdemir AT, Barshan B.

Sensors (Basel). 2014 Jun 18;14(6):10691-708. doi: 10.3390/s140610691.

10.

Using mobile phones for activity recognition in Parkinson's patients.

Albert MV, Toledo S, Shapiro M, Kording K.

Front Neurol. 2012 Nov 7;3:158. doi: 10.3389/fneur.2012.00158. eCollection 2012.

11.

Validity of a Smartphone-Based Fall Detection Application on Different Phones Worn on a Belt or in a Trouser Pocket.

Vermeulen J, Willard S, Aguiar B, De Witte LP.

Assist Technol. 2015 Spring;27(1):18-23. doi: 10.1080/10400435.2014.949015.

PMID:
26132221
12.

Evaluation of accelerometer-based fall detection algorithms on real-world falls.

Bagalà F, Becker C, Cappello A, Chiari L, Aminian K, Hausdorff JM, Zijlstra W, Klenk J.

PLoS One. 2012;7(5):e37062. doi: 10.1371/journal.pone.0037062. Epub 2012 May 16.

13.

Classification of radiology reports for falls in an HIV study cohort.

Bates J, Fodeh SJ, Brandt CA, Womack JA.

J Am Med Inform Assoc. 2016 Apr;23(e1):e113-7. doi: 10.1093/jamia/ocv155. Epub 2015 Nov 13.

14.

Classification of older adults with/without a fall history using machine learning methods.

Lin Zhang, Ou Ma, Fabre JM, Wood RH, Garcia SU, Ivey KM, McCann ED.

Conf Proc IEEE Eng Med Biol Soc. 2015;2015:6760-3. doi: 10.1109/EMBC.2015.7319945.

PMID:
26737845
15.

Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors.

Becker C, Schwickert L, Mellone S, Bagalà F, Chiari L, Helbostad JL, Zijlstra W, Aminian K, Bourke A, Todd C, Bandinelli S, Kerse N, Klenk J; FARSEEING Consortium; FARSEEING Meta Database Consensus Group.

Z Gerontol Geriatr. 2012 Dec;45(8):707-15. doi: 10.1007/s00391-012-0403-6. Review.

PMID:
23184296
16.

Fall related injuries: a retrospective medical review study in North India.

Jagnoor J, Keay L, Ganguli A, Dandona R, Thakur JS, Boufous S, Cumming R, Ivers RQ.

Injury. 2012 Dec;43(12):1996-2000. doi: 10.1016/j.injury.2011.08.004. Epub 2011 Sep 3.

PMID:
21893315
17.

Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles.

Saeb S, Cybulski TR, Schueller SM, Kording KP, Mohr DC.

J Med Internet Res. 2017 Apr 18;19(4):e118. doi: 10.2196/jmir.6821. Erratum in: 10.2196/jmir.7932.

18.

CDC's research portfolio in older adult fall prevention: a review of progress, 1985-2005, and future research directions.

Sleet DA, Moffett DB, Stevens J.

J Safety Res. 2008;39(3):259-67. doi: 10.1016/j.jsr.2008.05.003. Epub 2008 Jun 6.

PMID:
18571566
19.

[A study on fall accident].

Lee HS, Kim MJ.

Taehan Kanho. 1997 Nov-Dec;36(5):45-62. Korean.

PMID:
10437605
20.

Determination of simple thresholds for accelerometry-based parameters for fall detection.

Kangas M, Konttila A, Winblad I, Jämsä T.

Conf Proc IEEE Eng Med Biol Soc. 2007;2007:1367-70.

PMID:
18002218

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