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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

392 related items for PubMed ID: 26673129

  • 1. Performance of Activity Classification Algorithms in Free-Living Older Adults.
    Sasaki JE, Hickey AM, Staudenmayer JW, John D, Kent JA, Freedson PS.
    Med Sci Sports Exerc; 2016 May; 48(5):941-50. PubMed ID: 26673129
    [Abstract] [Full Text] [Related]

  • 2. 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
    [Abstract] [Full Text] [Related]

  • 3. 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
    [Abstract] [Full Text] [Related]

  • 4. 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; 20(1):75-80. PubMed ID: 27372275
    [Abstract] [Full Text] [Related]

  • 5. 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
    [Abstract] [Full Text] [Related]

  • 6. 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
    [Abstract] [Full Text] [Related]

  • 7. 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 05; 35(11):2191-203. PubMed ID: 25340969
    [Abstract] [Full Text] [Related]

  • 8. Intermonitor reliability of the GT3X+ accelerometer at hip, wrist and ankle sites during activities of daily living.
    Ozemek C, Kirschner MM, Wilkerson BS, Byun W, Kaminsky LA.
    Physiol Meas; 2014 Feb 05; 35(2):129-38. PubMed ID: 24399138
    [Abstract] [Full Text] [Related]

  • 9. 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
    [Abstract] [Full Text] [Related]

  • 10. Activity recognition using a single accelerometer placed at the wrist or ankle.
    Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W.
    Med Sci Sports Exerc; 2013 Nov 05; 45(11):2193-203. PubMed ID: 23604069
    [Abstract] [Full Text] [Related]

  • 11. Physical activity classification using the GENEA wrist-worn accelerometer.
    Zhang S, Rowlands AV, Murray P, Hurst TL.
    Med Sci Sports Exerc; 2012 Apr 05; 44(4):742-8. PubMed ID: 21988935
    [Abstract] [Full Text] [Related]

  • 12. Validation of automatic wear-time detection algorithms in a free-living setting of wrist-worn and hip-worn ActiGraph GT3X.
    Knaier R, Höchsmann C, Infanger D, Hinrichs T, Schmidt-Trucksäss A.
    BMC Public Health; 2019 Feb 28; 19(1):244. PubMed ID: 30819148
    [Abstract] [Full Text] [Related]

  • 13. 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
    [Abstract] [Full Text] [Related]

  • 14. 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; 15(1):105. PubMed ID: 30442154
    [Abstract] [Full Text] [Related]

  • 15. Comparability of measured acceleration from accelerometry-based activity monitors.
    Rowlands AV, Fraysse F, Catt M, Stiles VH, Stanley RM, Eston RG, Olds TS.
    Med Sci Sports Exerc; 2015 Jan 15; 47(1):201-10. PubMed ID: 24870577
    [Abstract] [Full Text] [Related]

  • 16. Effect of sampling rate on acceleration and counts of hip- and wrist-worn ActiGraph accelerometers in children.
    Clevenger KA, Pfeiffer KA, Mackintosh KA, McNarry MA, Brønd J, Arvidsson D, Montoye AHK.
    Physiol Meas; 2019 Sep 30; 40(9):095008. PubMed ID: 31518999
    [Abstract] [Full Text] [Related]

  • 17. Objective Assessment of Physical Activity: Classifiers for Public Health.
    Kerr J, Patterson RE, Ellis K, Godbole S, Johnson E, Lanckriet G, Staudenmayer J.
    Med Sci Sports Exerc; 2016 May 30; 48(5):951-7. PubMed ID: 27089222
    [Abstract] [Full Text] [Related]

  • 18. 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 May 30; 11(10):e0164045. PubMed ID: 27706241
    [Abstract] [Full Text] [Related]

  • 19. Machine learning for activity recognition: hip versus wrist data.
    Trost SG, Zheng Y, Wong WK.
    Physiol Meas; 2014 Nov 30; 35(11):2183-9. PubMed ID: 25340887
    [Abstract] [Full Text] [Related]

  • 20. Evaluation of raw acceleration sedentary thresholds in children and adults.
    Hildebrand M, Hansen BH, van Hees VT, Ekelund U.
    Scand J Med Sci Sports; 2017 Dec 30; 27(12):1814-1823. PubMed ID: 27878845
    [Abstract] [Full Text] [Related]

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