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Results: 1 to 20 of 103

1.

Distinguishing near-falls from daily activities with wearable accelerometers and gyroscopes using Support Vector Machines.

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

Conf Proc IEEE Eng Med Biol Soc. 2012;2012:5837-40. doi: 10.1109/EMBC.2012.6347321.

PMID:
23367256
[PubMed - indexed for MEDLINE]
2.

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

PMID:
24148648
[PubMed - in process]
3.

An analysis of the accuracy of wearable sensors for classifying the causes of falls in humans.

Aziz O, Robinovitch SN.

IEEE Trans Neural Syst Rehabil Eng. 2011 Dec;19(6):670-6. doi: 10.1109/TNSRE.2011.2162250. Epub 2011 Aug 22.

PMID:
21859608
[PubMed - indexed for MEDLINE]
Free PMC Article
4.

Optimal placement of accelerometers for the detection of everyday activities.

Cleland I, Kikhia B, Nugent C, Boytsov A, Hallberg J, Synnes K, McClean S, Finlay D.

Sensors (Basel). 2013 Jul 17;13(7):9183-200. doi: 10.3390/s130709183.

PMID:
23867744
[PubMed - indexed for MEDLINE]
Free PMC Article
5.

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.

PMID:
22586477
[PubMed - indexed for MEDLINE]
Free PMC Article
6.

Fall detection with the support vector machine during scripted and continuous unscripted activities.

Liu SH, Cheng WC.

Sensors (Basel). 2012;12(9):12301-16. doi: 10.3390/s120912301. Epub 2012 Sep 7.

PMID:
23112713
[PubMed - indexed for MEDLINE]
Free PMC Article
7.

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. Epub 2010 Aug 30.

PMID:
20805056
[PubMed - indexed for MEDLINE]
8.

Triaxial accelerometer-based fall detection method using a self-constructing cascade-AdaBoost-SVM classifier.

Cheng WC, Jhan DM.

IEEE J Biomed Health Inform. 2013 Mar;17(2):411-9. doi: 10.1109/JBHI.2012.2237034.

PMID:
24235113
[PubMed - indexed for MEDLINE]
9.

Assessing fall risk using wearable sensors: a practical discussion. A review of the practicalities and challenges associated with the use of wearable sensors for quantification of fall risk in older people.

Shany T, Redmond SJ, Marschollek M, Lovell NH.

Z Gerontol Geriatr. 2012 Dec;45(8):694-706. doi: 10.1007/s00391-012-0407-2. Review.

PMID:
23184295
[PubMed - indexed for MEDLINE]
10.

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
[PubMed - indexed for MEDLINE]
11.

Inoculation against falls: rapid adaptation by young and older adults to slips during daily activities.

Pai YC, Bhatt T, Wang E, Espy D, Pavol MJ.

Arch Phys Med Rehabil. 2010 Mar;91(3):452-9. doi: 10.1016/j.apmr.2009.10.032.

PMID:
20298839
[PubMed - indexed for MEDLINE]
Free PMC Article
12.

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
[PubMed - indexed for MEDLINE]
13.

Skin-contact sensor for automatic fall detection.

Narasimhan R.

Conf Proc IEEE Eng Med Biol Soc. 2012;2012:4038-41. doi: 10.1109/EMBC.2012.6346853.

PMID:
23366814
[PubMed - indexed for MEDLINE]
14.

Detecting falls with wearable sensors using machine learning techniques.

Ozdemir AT, Barshan B.

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

PMID:
24945676
[PubMed - in process]
Free Article
15.

Falls event detection using triaxial accelerometry and barometric pressure measurement.

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

Conf Proc IEEE Eng Med Biol Soc. 2009;2009:6111-4. doi: 10.1109/IEMBS.2009.5334922.

PMID:
19965262
[PubMed - indexed for MEDLINE]
16.
17.

Evaluation of a fall detector based on accelerometers: a pilot study.

Lindemann U, Hock A, Stuber M, Keck W, Becker C.

Med Biol Eng Comput. 2005 Sep;43(5):548-51.

PMID:
16411625
[PubMed - indexed for MEDLINE]
18.

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.

PMID:
22615890
[PubMed - indexed for MEDLINE]
Free PMC Article
19.

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
[PubMed - indexed for MEDLINE]
20.

Design of an unobtrusive wireless sensor network for nighttime falls detection.

Zhang Z, Kapoor U, Narayanan M, Lovell NH, Redmond SJ.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5275-8. doi: 10.1109/IEMBS.2011.6091305.

PMID:
22255528
[PubMed - indexed for MEDLINE]

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