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 *

157 related articles for article (PubMed ID: 32640526)

  • 1. 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]  

  • 2. A Study of One-Class Classification Algorithms for Wearable Fall Sensors.
    Santoyo-Ramón JA; Casilari E; Cano-García JM
    Biosensors (Basel); 2021 Aug; 11(8):. PubMed ID: 34436087
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.
    Šeketa G; Pavlaković L; Džaja D; Lacković I; Magjarević R
    Sensors (Basel); 2021 Jun; 21(13):. PubMed ID: 34202820
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. SisFall: A Fall and Movement Dataset.
    Sucerquia A; López JD; Vargas-Bonilla JF
    Sensors (Basel); 2017 Jan; 17(1):. PubMed ID: 28117691
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. Deep Learning-Based Near-Fall Detection Algorithm for Fall Risk Monitoring System Using a Single Inertial Measurement Unit.
    Choi A; Kim TH; Yuhai O; Jeong S; Kim K; Kim H; Mun JH
    IEEE Trans Neural Syst Rehabil Eng; 2022; 30():2385-2394. PubMed ID: 35969550
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Reliable Fall Detection System Based on Analyzing the Physical Activities of Older Adults Living in Long-Term Care Facilities.
    Saleh M; Abbas M; Prud'Homm J; Somme D; Le Bouquin Jeannes R
    IEEE Trans Neural Syst Rehabil Eng; 2021; 29():2587-2594. PubMed ID: 34874864
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Implementation of a real-time fall detection system based on hybrid threshold analysis algorithm and machine learning algorithm.
    Xu Y; He Z; Zhang X; Li D; Li R; Ni W
    Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():4205-4209. PubMed ID: 36085845
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Accelerometer and Camera-Based Strategy for Improved Human Fall Detection.
    Zerrouki N; Harrou F; Sun Y; Houacine A
    J Med Syst; 2016 Dec; 40(12):284. PubMed ID: 27796842
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Fall Detection for the Elderly Based on 3-Axis Accelerometer and Depth Sensor Fusion with Random Forest Classifier.
    Kim K; Yun G; Park SK; Kim DH
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():4611-4614. PubMed ID: 31946891
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System.
    Saadeh W; Butt SA; Altaf MAB
    IEEE Trans Neural Syst Rehabil Eng; 2019 May; 27(5):995-1003. PubMed ID: 30998473
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.