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 *

246 related articles for article (PubMed ID: 31627335)

  • 1. Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data.
    Chowdhury AK; Tjondronegoro D; Chandran V; Zhang J; Trost SG
    Sensors (Basel); 2019 Oct; 19(20):. PubMed ID: 31627335
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy.
    Ahmadi M; O'Neil M; Fragala-Pinkham M; Lennon N; Trost S
    J Neuroeng Rehabil; 2018 Nov; 15(1):105. PubMed ID: 30442154
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Cross-validation and out-of-sample testing of physical activity intensity predictions with a wrist-worn accelerometer.
    Montoye AHK; Westgate BS; Fonley MR; Pfeiffer KA
    J Appl Physiol (1985); 2018 May; 124(5):1284-1293. PubMed ID: 29369742
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Sensor-enabled Activity Class Recognition in Preschoolers: Hip versus Wrist Data.
    Trost SG; Cliff DP; Ahmadi MN; Tuc NV; Hagenbuchner M
    Med Sci Sports Exerc; 2018 Mar; 50(3):634-641. PubMed ID: 29059107
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer.
    Biró A; Szilágyi SM; Szilágyi L; Martín-Martín J; Cuesta-Vargas AI
    Sensors (Basel); 2023 Mar; 23(7):. PubMed ID: 37050655
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms.
    Chong J; Tjurin P; Niemelä M; Jämsä T; Farrahi V
    Gait Posture; 2021 Sep; 89():45-53. PubMed ID: 34225240
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study.
    Davidson P; Düking P; Zinner C; Sperlich B; Hotho A
    Sensors (Basel); 2020 May; 20(9):. PubMed ID: 32380738
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Automatic Identification of Physical Activity Intensity and Modality from the Fusion of Accelerometry and Heart Rate Data.
    García-García F; Benito PJ; Hernando ME
    Methods Inf Med; 2016 Dec; 55(6):533-544. PubMed ID: 27492483
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Free-living Evaluation of Laboratory-based Activity Classifiers in Preschoolers.
    Ahmadi MN; Brookes D; Chowdhury A; Pavey T; Trost SG
    Med Sci Sports Exerc; 2020 May; 52(5):1227-1234. PubMed ID: 31764460
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning.
    Na L; Yang C; Lo CC; Zhao F; Fukuoka Y; Aswani A
    JAMA Netw Open; 2018 Dec; 1(8):e186040. PubMed ID: 30646312
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of instantaneous perceived effort during outdoor running using accelerometry and machine learning.
    Pirscoveanu CI; Oliveira AS
    Eur J Appl Physiol; 2024 Mar; 124(3):963-973. PubMed ID: 37773522
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.
    Gilmore J; Nasseri M
    Sensors (Basel); 2024 May; 24(10):. PubMed ID: 38793858
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning-based classification of the movements of children with profound or severe intellectual or multiple disabilities using environment data features.
    Herbuela VRDM; Karita T; Furukawa Y; Wada Y; Toya A; Senba S; Onishi E; Saeki T
    PLoS One; 2022; 17(6):e0269472. PubMed ID: 35771797
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.
    Chowdhury AK; Tjondronegoro D; Chandran V; Trost SG
    Med Sci Sports Exerc; 2017 Sep; 49(9):1965-1973. PubMed ID: 28419025
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison of Accelerometry Methods for Estimating Physical Activity.
    Kerr J; Marinac CR; Ellis K; Godbole S; Hipp A; Glanz K; Mitchell J; Laden F; James P; Berrigan D
    Med Sci Sports Exerc; 2017 Mar; 49(3):617-624. PubMed ID: 27755355
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Feature selection for elderly faller classification based on wearable sensors.
    Howcroft J; Kofman J; Lemaire ED
    J Neuroeng Rehabil; 2017 May; 14(1):47. PubMed ID: 28558724
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification.
    Mendez KM; Reinke SN; Broadhurst DI
    Metabolomics; 2019 Nov; 15(12):150. PubMed ID: 31728648
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction of activity type in preschool children using machine learning techniques.
    Hagenbuchner M; Cliff DP; Trost SG; Van Tuc N; Peoples GE
    J Sci Med Sport; 2015 Jul; 18(4):426-31. PubMed ID: 25088983
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A comparison of subjective and objective measures of physical exertion.
    Skatrud-Mickelson M; Benson J; Hannon JC; Askew EW
    J Sports Sci; 2011 Dec; 29(15):1635-44. PubMed ID: 21995301
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.