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Items: 1 to 20 of 99

1.

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
2.

Fall detection with body-worn sensors : a systematic review.

Schwickert L, Becker C, Lindemann U, Maréchal C, Bourke A, Chiari L, Helbostad JL, Zijlstra W, Aminian K, Todd C, Bandinelli S, Klenk J; FARSEEING Consortium and the FARSEEING Meta Database Consensus Group.

Z Gerontol Geriatr. 2013 Dec;46(8):706-19. doi: 10.1007/s00391-013-0559-8. Review.

PMID:
24271251
3.

Development of a platform to combine sensor networks and home robots to improve fall detection in the home environment.

Della Toffola L, Patel S, Chen BR, Ozsecen YM, Puiatti A, Bonato P.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5331-4. doi: 10.1109/IEMBS.2011.6091319.

PMID:
22255542
4.

Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus.

Lamb SE, Jørstad-Stein EC, Hauer K, Becker C; Prevention of Falls Network Europe and Outcomes Consensus Group.

J Am Geriatr Soc. 2005 Sep;53(9):1618-22.

PMID:
16137297
5.

Fall detection devices and their use with older adults: a systematic review.

Chaudhuri S, Thompson H, Demiris G.

J Geriatr Phys Ther. 2014 Oct-Dec;37(4):178-96. doi: 10.1519/JPT.0b013e3182abe779. Review.

6.

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.

7.

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. Epub 2014 Aug 12.

PMID:
25112402
8.

iFall: an Android application for fall monitoring and response.

Sposaro F, Tyson G.

Conf Proc IEEE Eng Med Biol Soc. 2009;2009:6119-22. doi: 10.1109/IEMBS.2009.5334912.

PMID:
19965264
9.

Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor.

Tolkiehn M, Atallah L, Lo B, Yang GZ.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:369-72. doi: 10.1109/IEMBS.2011.6090120.

PMID:
22254325
10.

Prevention of falls and fall-related injuries in community-dwelling seniors: an evidence-based analysis.

Health Quality Ontario.

Ont Health Technol Assess Ser. 2008;8(2):1-78. Epub 2008 Oct 1.

11.

Simulated fall detection via accelerometers.

Boyle J, Karunanithi M.

Conf Proc IEEE Eng Med Biol Soc. 2008;2008:1274-7. doi: 10.1109/IEMBS.2008.4649396.

PMID:
19162899
12.

Fall prediction with wearable sensors--an empirical study on expert opinions.

Marschollek M, Schulze M, Gietzelt M, Lovel N, Redmond SJ.

Stud Health Technol Inform. 2013;190:138-40.

PMID:
23823402
13.

Temporal and kinematic variables for real-world falls harvested from lumbar sensors in the elderly population.

Bourke AK, Klenk J, Schwickert L, Aminian K, Ihlen EA, Helbostad JL, Chiari L, Becker C.

Conf Proc IEEE Eng Med Biol Soc. 2015;2015:5183-6. doi: 10.1109/EMBC.2015.7319559.

PMID:
26737459
14.

Fall detection algorithm for the elderly using acceleration sensors on the shoes.

Sim SY, Jeon HS, Chung GS, Kim SK, Kwon SJ, Lee WK, Park KS.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:4935-8. doi: 10.1109/IEMBS.2011.6091223.

PMID:
22255445
15.

Recommendations for collecting and processing accelerometry data in elderly people.

Ortlieb S, Gorzelniak L, Dias A, Schulz H, Horsch A.

Stud Health Technol Inform. 2013;192:1175.

PMID:
23920949
16.

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
17.

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. Epub 2011 Dec 12.

PMID:
22169389
18.

Sensor-based fall risk assessment - dagger of the mind?

Marschollek M, Schulze M, Gietzelt M, Lovell NH, Redmond SJ.

Stud Health Technol Inform. 2013;192:1048.

PMID:
23920822
19.

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
20.

Detecting falls with 3D range camera in ambient assisted living applications: a preliminary study.

Leone A, Diraco G, Siciliano P.

Med Eng Phys. 2011 Jul;33(6):770-81. doi: 10.1016/j.medengphy.2011.02.001. Epub 2011 Mar 5.

PMID:
21382737
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