These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

252 related articles for article (PubMed ID: 29786659)

  • 1. Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry.
    Nait Aicha A; Englebienne G; van Schooten KS; Pijnappels M; Kröse B
    Sensors (Basel); 2018 May; 18(5):. PubMed ID: 29786659
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A cross-dataset deep learning-based classifier for people fall detection and identification.
    Delgado-Escaño R; Castro FM; Cózar JR; Marín-Jiménez MJ; Guil N; Casilari E
    Comput Methods Programs Biomed; 2020 Feb; 184():105265. PubMed ID: 31881399
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A Novel Hybrid Deep Neural Network to Predict Pre-impact Fall for Older People Based on Wearable Inertial Sensors.
    Yu X; Qiu H; Xiong S
    Front Bioeng Biotechnol; 2020; 8():63. PubMed ID: 32117941
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning.
    Althobaiti T; Katsigiannis S; Ramzan N
    Sensors (Basel); 2020 Jul; 20(13):. PubMed ID: 32640526
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets.
    Casilari E; Lora-Rivera R; García-Lagos F
    Sensors (Basel); 2020 Mar; 20(5):. PubMed ID: 32155936
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prospective Fall-Risk Prediction Models for Older Adults Based on Wearable Sensors.
    Howcroft J; Kofman J; Lemaire ED
    IEEE Trans Neural Syst Rehabil Eng; 2017 Oct; 25(10):1812-1820. PubMed ID: 28358689
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Unobtrusive monitoring and identification of fall accidents.
    van de Ven P; O'Brien H; Nelson J; Clifford A
    Med Eng Phys; 2015 May; 37(5):499-504. PubMed ID: 25769224
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning.
    Martinez M; De Leon PL
    IEEE J Biomed Health Inform; 2020 Jan; 24(1):144-150. PubMed ID: 30932855
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Deep Learning for Fall Risk Assessment With Inertial Sensors: Utilizing Domain Knowledge in Spatio-Temporal Gait Parameters.
    Tunca C; Salur G; Ersoy C
    IEEE J Biomed Health Inform; 2020 Jul; 24(7):1994-2005. PubMed ID: 31831454
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Wearable Fall Detector Using Recurrent Neural Networks.
    Luna-Perejón F; Domínguez-Morales MJ; Civit-Balcells A
    Sensors (Basel); 2019 Nov; 19(22):. PubMed ID: 31717442
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Inertial sensing-based pre-impact detection of falls involving near-fall scenarios.
    Lee JK; Robinovitch SN; Park EJ
    IEEE Trans Neural Syst Rehabil Eng; 2015 Mar; 23(2):258-66. PubMed ID: 25252283
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine learning algorithms can classify outdoor terrain types during running using accelerometry data.
    Dixon PC; Schütte KH; Vanwanseele B; Jacobs JV; Dennerlein JT; Schiffman JM; Fournier PA; Hu B
    Gait Posture; 2019 Oct; 74():176-181. PubMed ID: 31539798
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Accuracy of a wavelet-based fall detection approach using an accelerometer and a barometric pressure sensor.
    Ejupi A; Galang C; Aziz O; Park EJ; Robinovitch S
    Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():2150-2153. PubMed ID: 29060322
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.
    Palmerini L; Klenk J; Becker C; Chiari L
    Sensors (Basel); 2020 Nov; 20(22):. PubMed ID: 33202738
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.
    Zhang J; Li Z; Liu Y; Li J; Qiu H; Li M; Hou G; Zhou Z
    J Med Internet Res; 2024 Aug; 26():e56750. PubMed ID: 39102676
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Fall-detection solution for mobile platforms using accelerometer and gyroscope data.
    De Cillisy F; De Simioy F; Guidoy F; Incalzi RA; Setolay R
    Annu Int Conf IEEE Eng Med Biol Soc; 2015 Aug; 2015():3727-30. PubMed ID: 26737103
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.