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Items: 1 to 20 of 142

1.

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
2.

[Sensor-based fall detection and prediction].

Marschollek M, Becker C.

Z Gerontol Geriatr. 2012 Dec;45(8):692-3. doi: 10.1007/s00391-012-0405-4. German. No abstract available.

PMID:
23184294
3.

GAL@Home: a feasibility study of sensor-based in-home fall detection.

Gietzelt M, Spehr J, Ehmen Y, Wegel S, Feldwieser F, Meis M, Marschollek M, Wolf KH, Steinhagen-Thiessen E, Gövercin M.

Z Gerontol Geriatr. 2012 Dec;45(8):716-21. doi: 10.1007/s00391-012-0400-9.

PMID:
23184297
4.

Smartphone-based solutions for fall detection and prevention: the FARSEEING approach.

Mellone S, Tacconi C, Schwickert L, Klenk J, Becker C, Chiari L.

Z Gerontol Geriatr. 2012 Dec;45(8):722-7. doi: 10.1007/s00391-012-0404-5.

PMID:
23184298
5.
6.

Exploration and comparison of the pre-impact lead time of active and passive falls based on inertial sensors.

Liang D, Ivanov K, Li H, Ning Y, Zhang Q, Wang L, Zhao G.

Biomed Mater Eng. 2014;24(1):279-88. doi: 10.3233/BME-130809.

PMID:
24211908
8.

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.

9.

Adherence to recommendations for fall prevention significantly affects the risk of falling after hip fracture: post-hoc analyses of a quasi-randomized controlled trial.

Di Monaco M, Vallero F, De Toma E, Castiglioni C, Gardin L, Giordano S, Tappero R.

Eur J Phys Rehabil Med. 2012 Mar;48(1):9-15.

PMID:
21785404
10.

Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects.

Kangas M, Vikman I, Nyberg L, Korpelainen R, Lindblom J, Jämsä T.

Gait Posture. 2012 Mar;35(3):500-5. doi: 10.1016/j.gaitpost.2011.11.016.

PMID:
22169389
11.

Unobtrusive monitoring and identification of fall accidents.

van de Ven P, O'Brien H, Nelson J, Clifford A.

Med Eng Phys. 2015 May;37(5):499-504. doi: 10.1016/j.medengphy.2015.02.009.

PMID:
25769224
12.

Multimodal sensor-based fall detection within the domestic environment of elderly people.

Feldwieser F, Gietzelt M, Goevercin M, Marschollek M, Meis M, Winkelbach S, Wolf KH, Spehr J, Steinhagen-Thiessen E.

Z Gerontol Geriatr. 2014 Dec;47(8):661-5. doi: 10.1007/s00391-014-0805-8.

PMID:
25112402
13.

Software simulation of unobtrusive falls detection at night-time using passive infrared and pressure mat sensors.

Ariani A, Redmond SJ, Chang D, Lovell NH.

Conf Proc IEEE Eng Med Biol Soc. 2010;2010:2115-8. doi: 10.1109/IEMBS.2010.5627202.

PMID:
21096573
14.

A wearable system for pre-impact fall detection.

Nyan MN, Tay FE, Murugasu E.

J Biomech. 2008 Dec 5;41(16):3475-81. doi: 10.1016/j.jbiomech.2008.08.009.

PMID:
18996529
15.

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.

16.

Development of a standard fall data format for signals from body-worn sensors : the FARSEEING consensus.

Klenk J, Chiari L, Helbostad JL, Zijlstra W, Aminian K, Todd C, Bandinelli S, Kerse N, Schwickert L, Mellone S, Bagalá F, Delbaere K, Hauer K, Redmond SJ, Robinovitch S, Aziz O, Schwenk M, Zecevic A, Zieschang T, Becker C; FARSEEING Consortium and the FARSEEING Meta-Database Consensus Group..

Z Gerontol Geriatr. 2013 Dec;46(8):720-6. doi: 10.1007/s00391-013-0554-0.

PMID:
24271252
17.

A risk model for the prediction of recurrent falls in community-dwelling elderly: a prospective cohort study.

Stalenhoef PA, Diederiks JP, Knottnerus JA, Kester AD, Crebolder HF.

J Clin Epidemiol. 2002 Nov;55(11):1088-94.

PMID:
12507672
18.

Barometric pressure and triaxial accelerometry-based falls event detection.

Bianchi F, Redmond SJ, Narayanan MR, Cerutti S, Lovell NH.

IEEE Trans Neural Syst Rehabil Eng. 2010 Dec;18(6):619-27. doi: 10.1109/TNSRE.2010.2070807.

PMID:
20805056
19.

[CBO guidelines to prevent accidental falls in the elderly: how can it be used in the institutionalized elderly?].

Neyens JC, Dijcks BP, de Kinkelder A, Graafmans WC, Schols JM.

Tijdschr Gerontol Geriatr. 2005 Sep;36(4):155-60. Review. Dutch.

PMID:
16194062
20.

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.

PMID:
24148648
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