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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

1032 related items for PubMed ID: 29369742

  • 1. 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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  • 2. 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 01; 20(1):75-80. PubMed ID: 27372275
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  • 3. 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 01; 35(11):2191-203. PubMed ID: 25340969
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  • 5. Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.
    Chowdhury AK, Tjondronegoro D, Chandran V, Trost SG.
    Med Sci Sports Exerc; 2017 Sep 01; 49(9):1965-1973. PubMed ID: 28419025
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  • 8. Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements.
    Staudenmayer J, He S, Hickey A, Sasaki J, Freedson P.
    J Appl Physiol (1985); 2015 Aug 15; 119(4):396-403. PubMed ID: 26112238
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  • 9. Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches.
    Bakrania K, Yates T, Rowlands AV, Esliger DW, Bunnewell S, Sanders J, Davies M, Khunti K, Edwardson CL.
    PLoS One; 2016 Aug 15; 11(10):e0164045. PubMed ID: 27706241
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  • 11. Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning.
    Mardini MT, Bai C, Wanigatunga AA, Saldana S, Casanova R, Manini TM.
    Sensors (Basel); 2021 May 12; 21(10):. PubMed ID: 34065906
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  • 12. AccNet24: A deep learning framework for classifying 24-hour activity behaviours from wrist-worn accelerometer data under free-living environments.
    Farrahi V, Muhammad U, Rostami M, Oussalah M.
    Int J Med Inform; 2023 Apr 12; 172():105004. PubMed ID: 36724729
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  • 13. Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.
    Ahmadi MN, Pavey TG, Trost SG.
    Sensors (Basel); 2020 Aug 05; 20(16):. PubMed ID: 32764316
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  • 14. 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 05; 47(8):1735-46. PubMed ID: 25494392
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  • 15. Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: implications of age.
    Strath SJ, Kate RJ, Keenan KG, Welch WA, Swartz AM.
    Physiol Meas; 2015 Nov 05; 36(11):2335-51. PubMed ID: 26449155
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  • 16. Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.
    Wullems JA, Verschueren SMP, Degens H, Morse CI, Onambélé GL.
    PLoS One; 2017 Nov 05; 12(11):e0188215. PubMed ID: 29155839
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  • 17. 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 05; 48(5):933-40. PubMed ID: 26673126
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  • 18. Machine learning to quantify habitual physical activity in children with cerebral palsy.
    Goodlich BI, Armstrong EL, Horan SA, Baque E, Carty CP, Ahmadi MN, Trost SG.
    Dev Med Child Neurol; 2020 Sep 05; 62(9):1054-1060. PubMed ID: 32420632
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  • 19. Validation and Comparison of Accelerometers Worn on the Hip, Thigh, and Wrists for Measuring Physical Activity and Sedentary Behavior.
    Montoye AHK, Pivarnik JM, Mudd LM, Biswas S, Pfeiffer KA.
    AIMS Public Health; 2016 Sep 05; 3(2):298-312. PubMed ID: 29546164
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  • 20. 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 05; 52(1):252-258. PubMed ID: 31361712
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