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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

142 related items for PubMed ID: 24271252

  • 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. PubMed ID: 24271252
    [Abstract] [Full Text] [Related]

  • 2. The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls.
    Klenk J, Schwickert L, Palmerini L, Mellone S, Bourke A, Ihlen EA, Kerse N, Hauer K, Pijnappels M, Synofzik M, Srulijes K, Maetzler W, Helbostad JL, Zijlstra W, Aminian K, Todd C, Chiari L, Becker C, FARSEEING Consortium.
    Eur Rev Aging Phys Act; 2016 Dec; 13():8. PubMed ID: 27807468
    [Abstract] [Full Text] [Related]

  • 3. 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. PubMed ID: 24271251
    [Abstract] [Full Text] [Related]

  • 4. Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study.
    Sczuka KS, Schwickert L, Becker C, Klenk J.
    J Med Internet Res; 2020 Apr 03; 22(4):e13961. PubMed ID: 32242825
    [Abstract] [Full Text] [Related]

  • 5. 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.
    Annu Int Conf IEEE Eng Med Biol Soc; 2015 Apr 03; 2015():5183-6. PubMed ID: 26737459
    [Abstract] [Full Text] [Related]

  • 6. Hidden Markov Model-Based Fall Detection With Motion Sensor Orientation Calibration: A Case for Real-Life Home Monitoring.
    Yu S, Chen H, Brown RA.
    IEEE J Biomed Health Inform; 2018 Nov 03; 22(6):1847-1853. PubMed ID: 29990227
    [Abstract] [Full Text] [Related]

  • 7. Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach.
    Bourke AK, Klenk J, Schwickert L, Aminian K, Ihlen EA, Mellone S, Helbostad JL, Chiari L, Becker C.
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug 03; 2016():3712-3715. PubMed ID: 28269098
    [Abstract] [Full Text] [Related]

  • 8. 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.
    Annu Int Conf IEEE Eng Med Biol Soc; 2011 Aug 03; 2011():5331-4. PubMed ID: 22255542
    [Abstract] [Full Text] [Related]

  • 9. Reading from the Black Box: What Sensors Tell Us about Resting and Recovery after Real-World Falls.
    Schwickert L, Klenk J, Zijlstra W, Forst-Gill M, Sczuka K, Helbostad JL, Chiari L, Aminian K, Todd C, Becker C.
    Gerontology; 2018 Aug 03; 64(1):90-95. PubMed ID: 28848150
    [Abstract] [Full Text] [Related]

  • 10. Developing the FARSEEING Taxonomy of Technologies: Classification and description of technology use (including ICT) in falls prevention studies.
    Boulton E, Hawley-Hague H, Vereijken B, Clifford A, Guldemond N, Pfeiffer K, Hall A, Chesani F, Mellone S, Bourke A, Todd C.
    J Biomed Inform; 2016 Jun 03; 61():132-40. PubMed ID: 27018213
    [Abstract] [Full Text] [Related]

  • 11. Fall detection devices and their use with older adults: a systematic review.
    Chaudhuri S, Thompson H, Demiris G.
    J Geriatr Phys Ther; 2014 Jun 03; 37(4):178-96. PubMed ID: 24406708
    [Abstract] [Full Text] [Related]

  • 12. Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor.
    Tolkiehn M, Atallah L, Lo B, Yang GZ.
    Annu Int Conf IEEE Eng Med Biol Soc; 2011 Jun 03; 2011():369-72. PubMed ID: 22254325
    [Abstract] [Full Text] [Related]

  • 13. iFall: an Android application for fall monitoring and response.
    Sposaro F, Tyson G.
    Annu Int Conf IEEE Eng Med Biol Soc; 2009 Jun 03; 2009():6119-22. PubMed ID: 19965264
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  • 16. 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.
    Annu Int Conf IEEE Eng Med Biol Soc; 2011 Jun 03; 2011():4935-8. PubMed ID: 22255445
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  • 19. 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 03; 53(9):1618-22. PubMed ID: 16137297
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