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 *

143 related articles for article (PubMed ID: 33429290)

  • 1. Machine learning sleep duration classification in Preschoolers using waist-worn ActiGraphs.
    Kuzik N; Spence JC; Carson V
    Sleep Med; 2021 Feb; 78():141-148. PubMed ID: 33429290
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Sleep classification from wrist-worn accelerometer data using random forests.
    Sundararajan K; Georgievska S; Te Lindert BHW; Gehrman PR; Ramautar J; Mazzotti DR; Sabia S; Weedon MN; van Someren EJW; Ridder L; Wang J; van Hees VT
    Sci Rep; 2021 Jan; 11(1):24. PubMed ID: 33420133
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Device-based measurement of physical activity in pre-schoolers: Comparison of machine learning and cut point methods.
    Ahmadi MN; Trost SG
    PLoS One; 2022; 17(4):e0266970. PubMed ID: 35417492
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. The relationship between machine-learning-derived sleep parameters and behavior problems in 3- and 5-year-old children: results from the CHILD Cohort study.
    Hammam N; Sadeghi D; Carson V; Tamana SK; Ezeugwu VE; Chikuma J; van Eeden C; Brook JR; Lefebvre DL; Moraes TJ; Subbarao P; Becker AB; Turvey SE; Sears MR; Mandhane PJ
    Sleep; 2020 Dec; 43(12):. PubMed ID: 32531021
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.
    Ellis K; Kerr J; Godbole S; Lanckriet G; Wing D; Marshall S
    Physiol Meas; 2014 Nov; 35(11):2191-203. PubMed ID: 25340969
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information.
    Logacjov A; Skarpsno ES; Kongsvold A; Bach K; Mork PJ
    Nat Sci Sleep; 2024; 16():699-710. PubMed ID: 38863481
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Hip and Wrist-Worn Accelerometer Data Analysis for Toddler Activities.
    Kwon S; Zavos P; Nickele K; Sugianto A; Albert MV
    Int J Environ Res Public Health; 2019 Jul; 16(14):. PubMed ID: 31330889
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.
    Zdravevski E; Risteska Stojkoska B; Standl M; Schulz H
    PLoS One; 2017; 12(9):e0184216. PubMed ID: 28880923
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Agreement in regard to total sleep time during a nap obtained via a sleep polygraph and accelerometer: a comparison of different sensitivity thresholds of the accelerometer.
    Kawada T; Suzuki H; Shimizu T; Katsumata M
    Int J Behav Med; 2012 Sep; 19(3):398-401. PubMed ID: 21744139
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers.
    Montoye AHK; Nelson MB; Bock JM; Imboden MT; Kaminsky LA; Mackintosh KA; McNarry MA; Pfeiffer KA
    Med Sci Sports Exerc; 2018 May; 50(5):1103-1112. PubMed ID: 29283934
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Identifying bedrest using 24-h waist or wrist accelerometry in adults.
    Tracy JD; Acra S; Chen KY; Buchowski MS
    PLoS One; 2018; 13(3):e0194461. PubMed ID: 29570740
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Identifying bedrest using waist-worn triaxial accelerometers in preschool children.
    Tracy JD; Donnelly T; Sommer EC; Heerman WJ; Barkin SL; Buchowski MS
    PLoS One; 2021; 16(1):e0246055. PubMed ID: 33507967
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A Dual-Accelerometer System for Detecting Human Movement in a Free-living Environment.
    Narayanan A; Stewart T; Mackay L
    Med Sci Sports Exerc; 2020 Jan; 52(1):252-258. PubMed ID: 31361712
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods.
    Kerr J; Carlson J; Godbole S; Cadmus-Bertram L; Bellettiere J; Hartman S
    Med Sci Sports Exerc; 2018 Jul; 50(7):1518-1524. PubMed ID: 29443824
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Fully automated waist-worn accelerometer algorithm for detecting children's sleep-period time separate from 24-h physical activity or sedentary behaviors.
    Tudor-Locke C; Barreira TV; Schuna JM; Mire EF; Katzmarzyk PT
    Appl Physiol Nutr Metab; 2014 Jan; 39(1):53-7. PubMed ID: 24383507
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.
    Ahmadi MN; Pavey TG; Trost SG
    Sensors (Basel); 2020 Aug; 20(16):. PubMed ID: 32764316
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees.
    Griffiths B; Diment L; Granat MH
    Sensors (Basel); 2021 Nov; 21(22):. PubMed ID: 34833534
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Comparison of a Commercial Accelerometer with Polysomnography and Actigraphy in Children and Adolescents.
    Meltzer LJ; Hiruma LS; Avis K; Montgomery-Downs H; Valentin J
    Sleep; 2015 Aug; 38(8):1323-30. PubMed ID: 26118555
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.