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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

536 related items for PubMed ID: 28159535

  • 1. 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; 20(8):761-765. PubMed ID: 28159535
    [Abstract] [Full Text] [Related]

  • 2. Accelerometer output and its association with energy expenditure during manual wheelchair propulsion.
    Learmonth YC, Kinnett-Hopkins D, Rice IM, Dysterheft JL, Motl RW.
    Spinal Cord; 2016 Feb; 54(2):110-4. PubMed ID: 25777327
    [Abstract] [Full Text] [Related]

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

  • 4. Triaxial accelerometer output predicts oxygen uptake in adults with Down syndrome.
    Allred AT, Choi P, Agiovlasitis S.
    Disabil Rehabil; 2021 Sep; 43(18):2602-2609. PubMed ID: 31880164
    [Abstract] [Full Text] [Related]

  • 5. Using accelerometry to classify physical activity intensity in older adults: What is the optimal wear-site?
    Duncan MJ, Rowlands A, Lawson C, Leddington Wright S, Hill M, Morris M, Eyre E, Tallis J.
    Eur J Sport Sci; 2020 Sep; 20(8):1131-1139. PubMed ID: 31726952
    [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
    [Abstract] [Full Text] [Related]

  • 7. Validation of the Vivago Wrist-Worn accelerometer in the assessment of physical activity.
    Vanhelst J, Hurdiel R, Mikulovic J, Bui-Xuân G, Fardy P, Theunynck D, Béghin L.
    BMC Public Health; 2012 Aug 22; 12():690. PubMed ID: 22913286
    [Abstract] [Full Text] [Related]

  • 8. Classification accuracy of the wrist-worn gravity estimator of normal everyday activity accelerometer.
    Welch WA, Bassett DR, Thompson DL, Freedson PS, Staudenmayer JW, John D, Steeves JA, Conger SA, Ceaser T, Howe CA, Sasaki JE, Fitzhugh EC.
    Med Sci Sports Exerc; 2013 Oct 22; 45(10):2012-9. PubMed ID: 23584403
    [Abstract] [Full Text] [Related]

  • 9. Predicting children's energy expenditure during physical activity using deep learning and wearable sensor data.
    Hamid A, Duncan MJ, Eyre ELJ, Jing Y.
    Eur J Sport Sci; 2021 Jun 22; 21(6):918-926. PubMed ID: 32597337
    [Abstract] [Full Text] [Related]

  • 10. Prediction of energy expenditure from wrist accelerometry in people with and without Down syndrome.
    Agiovlasitis S, Motl RW, Foley JT, Fernhall B.
    Adapt Phys Activ Q; 2012 Apr 22; 29(2):179-90. PubMed ID: 22467836
    [Abstract] [Full Text] [Related]

  • 11. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist.
    Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S.
    Med Sci Sports Exerc; 2013 May 22; 45(5):964-75. PubMed ID: 23247702
    [Abstract] [Full Text] [Related]

  • 12. 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 22; 68():141-149. PubMed ID: 30476691
    [Abstract] [Full Text] [Related]

  • 13. 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 22; 36(11):2335-51. PubMed ID: 26449155
    [Abstract] [Full Text] [Related]

  • 14. 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 22; 20(11):1003-1007. PubMed ID: 28483558
    [Abstract] [Full Text] [Related]

  • 15. Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheelchair users with spinal cord injury.
    García-Massó X, Serra-Añó P, García-Raffi LM, Sánchez-Pérez EA, López-Pascual J, Gonzalez LM.
    Spinal Cord; 2013 Dec 22; 51(12):898-903. PubMed ID: 23999111
    [Abstract] [Full Text] [Related]

  • 16. Prediction models discriminating between nonlocomotive and locomotive activities in children using a triaxial accelerometer with a gravity-removal physical activity classification algorithm.
    Hikihara Y, Tanaka C, Oshima Y, Ohkawara K, Ishikawa-Takata K, Tanaka S.
    PLoS One; 2014 Dec 22; 9(4):e94940. PubMed ID: 24755646
    [Abstract] [Full Text] [Related]

  • 17. Estimation of resistance exercise energy expenditure using triaxial accelerometry.
    Stec MJ, Rawson ES.
    J Strength Cond Res; 2012 May 22; 26(5):1413-22. PubMed ID: 22222328
    [Abstract] [Full Text] [Related]

  • 18. Estimating Physical Activity in Children Aged 8-11 Years Using Accelerometry: Contributions From Fundamental Movement Skills and Different Accelerometer Placements.
    Duncan MJ, Roscoe CMP, Faghy M, Tallis J, Eyre ELJ.
    Front Physiol; 2019 May 22; 10():242. PubMed ID: 30936837
    [Abstract] [Full Text] [Related]

  • 19. Age group comparability of raw accelerometer output from wrist- and hip-worn monitors.
    Hildebrand M, VAN Hees VT, Hansen BH, Ekelund U.
    Med Sci Sports Exerc; 2014 Sep 22; 46(9):1816-24. PubMed ID: 24887173
    [Abstract] [Full Text] [Related]

  • 20. Energy Cost of Lower Body Dressing, Pop-Over Transfers, and Manual Wheelchair Propulsion in People with Paraplegia Due to Motor-Complete Spinal Cord Injury.
    Lynch MM, McCormick Z, Liem B, Jacobs G, Hwang P, Hornby TG, Rydberg L, Roth EJ.
    Top Spinal Cord Inj Rehabil; 2015 Sep 22; 21(2):140-8. PubMed ID: 26364283
    [Abstract] [Full Text] [Related]

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