Combining novelty detectors to improve accelerometer-based fall detection-Reference-Cited by-同舟云学术

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Combining novelty detectors to improve accelerometer-based fall detection

Published:2017-03-01 Issue:10 Volume:55 Page:1849-1858
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Medrano Carlos,Igual Raúl^ORCID,García-Magariño Iván,Plaza Inmaculada,Azuara Guillermo

Funder

Secretaría de Estado de Investigación, Desarrollo e Innovación (ES)

Departamento de Innovación, Investigación y Universidad del Gobierno de Aragón

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

http://link.springer.com/article/10.1007/s11517-017-1632-z/fulltext.html

Reference31 articles.

1. Albert MV, Kording K, Herrmann M, Jayaraman A (2012) Fall classification by machine learning using mobile phones. PLoS ONE 7:e36556

2. Alpaydin E (2010) Introduction to machine learning. The MIT Press, Cambridge, p 537

3. Aziz O, Musngi M, Park EJ, Mori G, Robinovitch SN (2016) 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. Med Biol Eng Comput. doi: 10.1007/s11517-016-1504-y

4. Bagala F, Becker C, Cappello A, Chiari L, Aminian K, Hausdorff JM, Zijlstra W, Klenk J (2012) Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE 7:e37062

5. Bourke AK, O’Brien JV, Lyons GM (2007) Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2):194–199

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