185 related articles for article (PubMed ID: 22336100)
1. Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems.
Yuwono M; Moulton BD; Su SW; Celler BG; Nguyen HT
Biomed Eng Online; 2012 Feb; 11():9. PubMed ID: 22336100
[TBL] [Abstract][Full Text] [Related]
2. Detecting falls with wearable sensors using machine learning techniques.
Özdemir AT; Barshan B
Sensors (Basel); 2014 Jun; 14(6):10691-708. PubMed ID: 24945676
[TBL] [Abstract][Full Text] [Related]
3. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm.
Bourke AK; O'Brien JV; Lyons GM
Gait Posture; 2007 Jul; 26(2):194-9. PubMed ID: 17101272
[TBL] [Abstract][Full Text] [Related]
4. The design and development of a long-term fall detection system incorporated into a custom vest for the elderly.
Bourke AK; van de Ven PW; Chaya AE; OLaighin GM; Nelson J
Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():2836-9. PubMed ID: 19163296
[TBL] [Abstract][Full Text] [Related]
5. Threshold-based fall detection using a hybrid of tri-axial accelerometer and gyroscope.
Wang FT; Chan HL; Hsu MH; Lin CK; Chao PK; Chang YJ
Physiol Meas; 2018 Oct; 39(10):105002. PubMed ID: 30207983
[TBL] [Abstract][Full Text] [Related]
6. Sensitivity and specificity of fall detection in people aged 40 years and over.
Kangas M; Vikman I; Wiklander J; Lindgren P; Nyberg L; Jämsä T
Gait Posture; 2009 Jun; 29(4):571-4. PubMed ID: 19153043
[TBL] [Abstract][Full Text] [Related]
7. Assessment of waist-worn tri-axial accelerometer based fall-detection algorithms using continuous unsupervised activities.
Bourke AK; van de Ven P; Gamble M; O'Connor R; Murphy K; Bogan E; McQuade E; Finucane P; Olaighin G; Nelson J
Annu Int Conf IEEE Eng Med Biol Soc; 2010; 2010():2782-5. PubMed ID: 21095967
[TBL] [Abstract][Full Text] [Related]
8. Combining novelty detectors to improve accelerometer-based fall detection.
Medrano C; Igual R; García-Magariño I; Plaza I; Azuara G
Med Biol Eng Comput; 2017 Oct; 55(10):1849-1858. PubMed ID: 28251444
[TBL] [Abstract][Full Text] [Related]
9. 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; 2016():3712-3715. PubMed ID: 28269098
[TBL] [Abstract][Full Text] [Related]
10. Optimum gravity vector and vertical acceleration estimation using a tri-axial accelerometer for falls and normal activities.
Bourke AK; O'Donovan K; Clifford A; ÓLaighin G; Nelson J
Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():7896-9. PubMed ID: 22256171
[TBL] [Abstract][Full Text] [Related]
11. Determination of simple thresholds for accelerometry-based parameters for fall detection.
Kangas M; Konttila A; Winblad I; Jämsä T
Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():1367-70. PubMed ID: 18002218
[TBL] [Abstract][Full Text] [Related]
12. Testing of a long-term fall detection system incorporated into a custom vest for the elderly.
Bourke AK; van de Ven PW; Chaya AE; OLaighin GM; Nelson J
Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():2844-7. PubMed ID: 19163298
[TBL] [Abstract][Full Text] [Related]
13. Fall-detection through vertical velocity thresholding using a tri-axial accelerometer characterized using an optical motion-capture system.
Bourke AK; O'Donovan KJ; Nelson J; OLaighin GM
Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():2832-5. PubMed ID: 19163295
[TBL] [Abstract][Full Text] [Related]
14. 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.
Aziz O; Musngi M; Park EJ; Mori G; Robinovitch SN
Med Biol Eng Comput; 2017 Jan; 55(1):45-55. PubMed ID: 27106749
[TBL] [Abstract][Full Text] [Related]
15. Personalization and adaptation to the medium and context in a fall detection system.
Naranjo-Hernandez D; Roa LM; Reina-Tosina J; Estudillo-Valderrama MA
IEEE Trans Inf Technol Biomed; 2012 Mar; 16(2):264-71. PubMed ID: 22287249
[TBL] [Abstract][Full Text] [Related]
16. 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. PubMed ID: 20805056
[TBL] [Abstract][Full Text] [Related]
17. Implementation of accelerometer sensor module and fall detection monitoring system based on wireless sensor network.
Lee Y; Kim J; Son M; Lee M
Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():2315-8. PubMed ID: 18002455
[TBL] [Abstract][Full Text] [Related]
18. 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. PubMed ID: 24235113
[TBL] [Abstract][Full Text] [Related]
19. Inertial Data-Based AI Approaches for ADL and Fall Recognition.
Martins LM; Ribeiro NF; Soares F; Santos CP
Sensors (Basel); 2022 May; 22(11):. PubMed ID: 35684649
[TBL] [Abstract][Full Text] [Related]
20. Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets.
Aziz O; Klenk J; Schwickert L; Chiari L; Becker C; Park EJ; Mori G; Robinovitch SN
PLoS One; 2017; 12(7):e0180318. PubMed ID: 28678808
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]