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 *

133 related articles for article (PubMed ID: 28113297)

  • 1. Validation of Energy Expenditure Prediction Models Using Real-Time Shoe-Based Motion Detectors.
    Lin SY; Lai YC; Hsia CC; Su PF; Chang CH
    IEEE Trans Biomed Eng; 2017 Sep; 64(9):2152-2162. PubMed ID: 28113297
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning.
    Altini M; Penders J; Vullers R; Amft O
    IEEE J Biomed Health Inform; 2015 Jan; 19(1):219-26. PubMed ID: 24691168
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Accurate prediction of energy expenditure using a shoe-based activity monitor.
    Sazonova N; Browning RC; Sazonov E
    Med Sci Sports Exerc; 2011 Jul; 43(7):1312-21. PubMed ID: 21131868
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Posture and activity recognition and energy expenditure estimation in a wearable platform.
    Sazonov E; Hegde N; Browning RC; Melanson EL; Sazonova NA
    IEEE J Biomed Health Inform; 2015 Jul; 19(4):1339-46. PubMed ID: 26011870
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Evaluation of the activPAL accelerometer for physical activity and energy expenditure estimation in a semi-structured setting.
    Montoye AHK; Pivarnik JM; Mudd LM; Biswas S; Pfeiffer KA
    J Sci Med Sport; 2017 Nov; 20(11):1003-1007. PubMed ID: 28483558
    [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. An original piecewise model for computing energy expenditure from accelerometer and heart rate signals.
    Romero-Ugalde HM; Garnotel M; Doron M; Jallon P; Charpentier G; Franc S; Huneker E; Simon C; Bonnet S
    Physiol Meas; 2017 Jul; 38(8):1599-1615. PubMed ID: 28665293
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Estimation of Energy Expenditure for Wheelchair Users Using a Physical Activity Monitoring System.
    Hiremath SV; Intille SS; Kelleher A; Cooper RA; Ding D
    Arch Phys Med Rehabil; 2016 Jul; 97(7):1146-1153.e1. PubMed ID: 26976800
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Energy Expenditure Prediction Using Raw Accelerometer Data in Simulated Free Living.
    Montoye AH; Mudd LM; Biswas S; Pfeiffer KA
    Med Sci Sports Exerc; 2015 Aug; 47(8):1735-46. PubMed ID: 25494392
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Measurement of Physical Activity by Shoe-Based Accelerometers-Calibration and Free-Living Validation.
    Fridolfsson J; Arvidsson D; Grau S
    Sensors (Basel); 2021 Mar; 21(7):. PubMed ID: 33810616
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Simultaneous heart rate-motion sensor technique to estimate energy expenditure.
    Strath SJ; Bassett DR; Swartz AM; Thompson DL
    Med Sci Sports Exerc; 2001 Dec; 33(12):2118-23. PubMed ID: 11740308
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Relative validity of 3 accelerometer models for estimating energy expenditure during light activity.
    Wetten AA; Batterham M; Tan SY; Tapsell L
    J Phys Act Health; 2014 Mar; 11(3):638-47. PubMed ID: 23417054
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Use of heart rate to predict energy expenditure from low to high activity levels.
    Hiilloskorpi HK; Pasanen ME; Fogelholm MG; Laukkanen RM; Mänttäri AT
    Int J Sports Med; 2003 Jul; 24(5):332-6. PubMed ID: 12868043
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predictive validity of three ActiGraph energy expenditure equations for children.
    Trost SG; Way R; Okely AD
    Med Sci Sports Exerc; 2006 Feb; 38(2):380-7. PubMed ID: 16531910
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Personalized cardiorespiratory fitness and energy expenditure estimation using hierarchical Bayesian models.
    Altini M; Casale P; Penders J; Amft O
    J Biomed Inform; 2015 Aug; 56():195-204. PubMed ID: 26079263
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Validity of hip-mounted uniaxial accelerometry with heart-rate monitoring vs. triaxial accelerometry in the assessment of free-living energy expenditure in young children: the IDEFICS Validation Study.
    Ojiambo R; Konstabel K; Veidebaum T; Reilly J; Verbestel V; Huybrechts I; Sioen I; Casajús JA; Moreno LA; Vicente-Rodriguez G; Bammann K; Tubic BM; Marild S; Westerterp K; Pitsiladis YP;
    J Appl Physiol (1985); 2012 Nov; 113(10):1530-6. PubMed ID: 22995396
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Validation of a multi-sensor armband during free-living activity in adults with cystic fibrosis.
    Cox NS; Alison JA; Button BM; Wilson JW; Morton JM; Dowman LM; Holland AE
    J Cyst Fibros; 2014 May; 13(3):347-50. PubMed ID: 24374296
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.
    Montoye AHK; Begum M; Henning Z; Pfeiffer KA
    Physiol Meas; 2017 Feb; 38(2):343-357. PubMed ID: 28107205
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer.
    Qiao Wang ; Lohit S; Toledo MJ; Buman MP; Turaga P
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():2631-2635. PubMed ID: 28268862
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Energy expenditure estimation during normal ambulation using triaxial accelerometry and barometric pressure.
    Wang J; Redmond SJ; Voleno M; Narayanan MR; Wang N; Cerutti S; Lovell NH
    Physiol Meas; 2012 Nov; 33(11):1811-30. PubMed ID: 23110944
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.