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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

817 related items for PubMed ID: 25340969

  • 1. 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
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  • 2. 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
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  • 3. Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification.
    Ellis K, Kerr J, Godbole S, Staudenmayer J, Lanckriet G.
    Med Sci Sports Exerc; 2016 May; 48(5):933-40. PubMed ID: 26673126
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  • 4. Comparison of Indirect Calorimetry- and Accelerometry-Based Energy Expenditure During Children's Discrete Skill Performance.
    Sacko R, McIver K, Brazendale K, Pfeifer C, Brian A, Nesbitt D, Stodden DF.
    Res Q Exerc Sport; 2019 Dec; 90(4):629-640. PubMed ID: 31441713
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  • 5. Distributed lag and spline modeling for predicting energy expenditure from accelerometry in youth.
    Choi L, Chen KY, Acra SA, Buchowski MS.
    J Appl Physiol (1985); 2010 Feb; 108(2):314-27. PubMed ID: 19959770
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  • 6. 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 01; 124(5):1284-1293. PubMed ID: 29369742
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  • 8. 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 01; 47(8):1735-46. PubMed ID: 25494392
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  • 10. Wrist-Worn Activity Trackers in Laboratory and Free-Living Settings for Patients With Chronic Pain: Criterion Validity Study.
    Sjöberg V, Westergren J, Monnier A, Lo Martire R, Hagströmer M, Äng BO, Vixner L.
    JMIR Mhealth Uhealth; 2021 Jan 12; 9(1):e24806. PubMed ID: 33433391
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  • 11. Field evaluation of a random forest activity classifier for wrist-worn accelerometer data.
    Pavey TG, Gilson ND, Gomersall SR, Clark B, Trost SG.
    J Sci Med Sport; 2017 Jan 12; 20(1):75-80. PubMed ID: 27372275
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  • 13. Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study.
    O'Driscoll R, Turicchi J, Hopkins M, Duarte C, Horgan GW, Finlayson G, Stubbs RJ.
    JMIR Mhealth Uhealth; 2021 Aug 04; 9(8):e23938. PubMed ID: 34346890
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  • 18. Wrist-worn triaxial accelerometry predicts the energy expenditure of non-vigorous daily physical activities.
    Sirichana W, Dolezal BA, Neufeld EV, Wang X, Cooper CB.
    J Sci Med Sport; 2017 Aug 04; 20(8):761-765. PubMed ID: 28159535
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  • 20. Calibration of wrist-worn ActiWatch 2 and ActiGraph wGT3X for assessment of physical activity in young adults.
    Lee P, Tse CY.
    Gait Posture; 2019 Feb 04; 68():141-149. PubMed ID: 30476691
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