These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

159 related articles for article (PubMed ID: 30537957)

  • 21. Validation of a wireless accelerometer network for energy expenditure measurement.
    Montoye AH; Dong B; Biswas S; Pfeiffer KA
    J Sports Sci; 2016 Nov; 34(21):2130-9. PubMed ID: 26942316
    [TBL] [Abstract][Full Text] [Related]  

  • 22. A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer.
    Qiao Wang ; Lohit S; Toledo MJ; Buman MP; Turaga P
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():2631-2635. PubMed ID: 28268862
    [TBL] [Abstract][Full Text] [Related]  

  • 23. 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; 16(14):. PubMed ID: 31330889
    [TBL] [Abstract][Full Text] [Related]  

  • 24. 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; 36(11):2335-51. PubMed ID: 26449155
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Examining accelerometer validity for estimating physical activity in pre-schoolers during free-living activity.
    Dobell AP; Eyre ELJ; Tallis J; Chinapaw MJM; Altenburg TM; Duncan MJ
    Scand J Med Sci Sports; 2019 Oct; 29(10):1618-1628. PubMed ID: 31206785
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Deciphering the constrained total energy expenditure model in humans by associating accelerometer-measured physical activity from wrist and hip.
    Fernández-Verdejo R; Alcantara JMA; Galgani JE; Acosta FM; Migueles JH; Amaro-Gahete FJ; Labayen I; Ortega FB; Ruiz JR
    Sci Rep; 2021 Jun; 11(1):12302. PubMed ID: 34112912
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.
    Ahmadi MN; Pavey TG; Trost SG
    Sensors (Basel); 2020 Aug; 20(16):. PubMed ID: 32764316
    [TBL] [Abstract][Full Text] [Related]  

  • 28. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Prediction of activity type in preschool children using machine learning techniques.
    Hagenbuchner M; Cliff DP; Trost SG; Van Tuc N; Peoples GE
    J Sci Med Sport; 2015 Jul; 18(4):426-31. PubMed ID: 25088983
    [TBL] [Abstract][Full Text] [Related]  

  • 30. 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; 52(1):252-258. PubMed ID: 31361712
    [TBL] [Abstract][Full Text] [Related]  

  • 31. 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; 35(2):129-38. PubMed ID: 24399138
    [TBL] [Abstract][Full Text] [Related]  

  • 32. 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; 45(11):2193-203. PubMed ID: 23604069
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Calibration and validation of accelerometer-based activity monitors: A systematic review of machine-learning approaches.
    Farrahi V; Niemelä M; Kangas M; Korpelainen R; Jämsä T
    Gait Posture; 2019 Feb; 68():285-299. PubMed ID: 30579037
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Validation of accelerometer placement to capture energy expenditure using doubly labeled water.
    Dougherty RJ; Liu F; Etzkorn L; Wanigatunga AA; Walter PJ; Knuth ND; Schrack JA; Ferrucci L
    Appl Physiol Nutr Metab; 2022 Oct; 47(10):1045-1049. PubMed ID: 35939837
    [TBL] [Abstract][Full Text] [Related]  

  • 35. 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; 11(10):e0164045. PubMed ID: 27706241
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Estimation of Energy Expenditure for Wheelchair Users Using a Physical Activity Monitoring System.
    Hiremath SV; Intille SS; Kelleher A; Cooper RA; Ding D
    Arch Phys Med Rehabil; 2016 Jul; 97(7):1146-1153.e1. PubMed ID: 26976800
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Wrist-Worn Accelerometer-Brand Independent Posture Classification.
    Rowlands AV; Yates T; Olds TS; Davies M; Khunti K; Edwardson CL
    Med Sci Sports Exerc; 2016 Apr; 48(4):748-54. PubMed ID: 26559451
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Comparison of Accelerometry Methods for Estimating Physical Activity.
    Kerr J; Marinac CR; Ellis K; Godbole S; Hipp A; Glanz K; Mitchell J; Laden F; James P; Berrigan D
    Med Sci Sports Exerc; 2017 Mar; 49(3):617-624. PubMed ID: 27755355
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Predicting physical activity energy expenditure in manual wheelchair users.
    Nightingale TE; Walhim JP; Thompson D; Bilzon JL
    Med Sci Sports Exerc; 2014 Sep; 46(9):1849-58. PubMed ID: 25134004
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Relating wrist accelerometry measures to disability in older adults.
    Huisingh-Scheetz MJ; Kocherginsky M; Magett E; Rush P; Dale W; Waite L
    Arch Gerontol Geriatr; 2016; 62():68-74. PubMed ID: 26452423
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 8.