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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

817 related items for PubMed ID: 25340969

  • 41. Classification of physical activity intensities using a wrist-worn accelerometer in 8-12-year-old children.
    Chandler JL, Brazendale K, Beets MW, Mealing BA.
    Pediatr Obes; 2016 Apr; 11(2):120-7. PubMed ID: 25893950
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  • 42. Artificial neural networks to predict activity type and energy expenditure in youth.
    Trost SG, Wong WK, Pfeiffer KA, Zheng Y.
    Med Sci Sports Exerc; 2012 Sep; 44(9):1801-9. PubMed ID: 22525766
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  • 45. Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study.
    Davoudi A, Wanigatunga AA, Kheirkhahan M, Corbett DB, Mendoza T, Battula M, Ranka S, Fillingim RB, Manini TM, Rashidi P.
    JMIR Mhealth Uhealth; 2019 Feb 06; 7(2):e11270. PubMed ID: 30724739
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  • 46. 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
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  • 47. Use of a Wireless Network of Accelerometers for Improved Measurement of Human Energy Expenditure.
    Montoye AH, Dong B, Biswas S, Pfeiffer KA.
    Electronics (Basel); 2014 Nov 15; 3(2):205-220. PubMed ID: 25530874
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  • 48. Accuracy of the Multisensory Wristwatch Polar Vantage's Estimation of Energy Expenditure in Various Activities: Instrument Validation Study.
    Gilgen-Ammann R, Schweizer T, Wyss T.
    JMIR Mhealth Uhealth; 2019 Oct 02; 7(10):e14534. PubMed ID: 31579020
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  • 49. Influence of accelerometer type and placement on physical activity energy expenditure prediction in manual wheelchair users.
    Nightingale TE, Walhin JP, Thompson D, Bilzon JL.
    PLoS One; 2015 Oct 02; 10(5):e0126086. PubMed ID: 25955304
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  • 50. Validation of the Fitbit One® for physical activity measurement at an upper torso attachment site.
    Diaz KM, Krupka DJ, Chang MJ, Shaffer JA, Ma Y, Goldsmith J, Schwartz JE, Davidson KW.
    BMC Res Notes; 2016 Apr 12; 9():213. PubMed ID: 27068022
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  • 51. Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer.
    Xu L, Zhang J, Li Z, Liu Y, Jia Z, Han X, Liu C, Zhou Z.
    Front Physiol; 2023 Apr 12; 14():1202737. PubMed ID: 38028785
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  • 54. Hip and Wrist-Worn Accelerometer Data Analysis for Toddler Activities.
    Kwon S, Zavos P, Nickele K, Sugianto A, Albert MV.
    Int J Environ Res Public Health; 2019 Jul 21; 16(14):. PubMed ID: 31330889
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  • 56. Validity of physical activity intensity predictions by ActiGraph, Actical, and RT3 accelerometers.
    Rothney MP, Schaefer EV, Neumann MM, Choi L, Chen KY.
    Obesity (Silver Spring); 2008 Aug 21; 16(8):1946-52. PubMed ID: 18535553
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  • 57. Wear Compliance and Activity in Children Wearing Wrist- and Hip-Mounted Accelerometers.
    Fairclough SJ, Noonan R, Rowlands AV, Van Hees V, Knowles Z, Boddy LM.
    Med Sci Sports Exerc; 2016 Feb 21; 48(2):245-53. PubMed ID: 26375253
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  • 59. Comparison of physical activity assessed using hip- and wrist-worn accelerometers.
    Kamada M, Shiroma EJ, Harris TB, Lee IM.
    Gait Posture; 2016 Feb 21; 44():23-8. PubMed ID: 27004628
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