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Results: 1 to 20 of 134

Similar articles for PubMed (Select 22467836)

1.

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;29(2):179-90.

PMID:
22467836
2.

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. doi: 10.1088/0967-3334/35/11/2191. Epub 2014 Oct 23.

PMID:
25340969
3.

Hierarchical approaches to estimate energy expenditure using phone-based accelerometers.

Vathsangam H, Schroeder ET, Sukhatme GS.

IEEE J Biomed Health Inform. 2014 Jul;18(4):1242-52. doi: 10.1109/JBHI.2013.2297055.

PMID:
25014933
4.

Predicting METs from the heart rate index in persons with Down syndrome.

Agiovlasitis S, Rossow LM, Yan H, Ranadive SM, Fahs CA, Motl RW, Fernhall B.

Res Dev Disabil. 2014 Oct;35(10):2423-9. doi: 10.1016/j.ridd.2014.06.005. Epub 2014 Jun 28.

PMID:
24981191
5.

Different location of triaxial accelerometer and different energy expenditures.

Kim do Y, Jung YS, Park RW, Joo NS.

Yonsei Med J. 2014 Jul;55(4):1145-51. doi: 10.3349/ymj.2014.55.4.1145.

6.

Mechanical energy assessment of adult with Down syndrome during walking with obstacle avoidance.

Salami F, Vimercati SL, Rigoldi C, Taebi A, Albertini G, Galli M.

Res Dev Disabil. 2014 Aug;35(8):1856-62. doi: 10.1016/j.ridd.2014.04.012. Epub 2014 Apr 30.

PMID:
24794319
7.

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 Apr 22;9(4):e94940. doi: 10.1371/journal.pone.0094940. eCollection 2014.

8.

Accelerometer cut-points derived during over-ground walking in persons with mild, moderate, and severe multiple sclerosis.

Sandroff BM, Riskin BJ, Agiovlasitis S, Motl RW.

J Neurol Sci. 2014 May 15;340(1-2):50-7. doi: 10.1016/j.jns.2014.02.024. Epub 2014 Feb 28.

PMID:
24635890
9.

Exercise intensities of gardening tasks within older adult allotment gardeners in Wales.

Hawkins JL, Smith A, Backx K, Clayton DA.

J Aging Phys Act. 2015 Apr;23(2):161-8. doi: 10.1123/japa.2013-0171. Epub 2014 Feb 28.

PMID:
24589559
10.

Combined triaxial accelerometry and heart rate telemetry for the physiological characterization of Latin dance in non-professional adults.

Domene PA, Easton C.

J Dance Med Sci. 2014 Mar;18(1):29-36. doi: 10.12678/1089-313X.18.1.29.

PMID:
24568801
11.

Step-rate thresholds for physical activity intensity in persons with multiple sclerosis.

Agiovlasitis S, Motl RW.

Adapt Phys Activ Q. 2014 Jan;31(1):4-18. doi: 10.1123/apaq:2013-0008.

PMID:
24385438
12.

Metabolic equivalent determination in the cultural dance of hula.

Usagawa T, Look M, de Silva M, Stickley C, Kaholokula JK, Seto T, Mau M.

Int J Sports Med. 2014 May;35(5):399-402. doi: 10.1055/s-0033-1353213. Epub 2013 Nov 7.

13.

Towards valid estimates of activity energy expenditure using an accelerometer: searching for a proper analytical strategy and big data.

Bonomi AG.

J Appl Physiol (1985). 2013 Nov 1;115(9):1227-8. doi: 10.1152/japplphysiol.01028.2013. Epub 2013 Sep 12. No abstract available.

14.

Neural network versus activity-specific prediction equations for energy expenditure estimation in children.

Ruch N, Joss F, Jimmy G, Melzer K, Hänggi J, Mäder U.

J Appl Physiol (1985). 2013 Nov 1;115(9):1229-36. doi: 10.1152/japplphysiol.01443.2012. Epub 2013 Aug 29.

15.

Energy expenditure during activity in the American lobster Homarus americanus: Correlations with body acceleration.

Lyons GN, Halsey LG, Pope EC, Eddington JD, Houghton JD.

Comp Biochem Physiol A Mol Integr Physiol. 2013 Oct;166(2):278-84. doi: 10.1016/j.cbpa.2013.06.024. Epub 2013 Jun 28.

PMID:
23811045
16.

Use of accelerometry to classify activity beneficial to bone in premenopausal women.

Stiles VH, Griew PJ, Rowlands AV.

Med Sci Sports Exerc. 2013 Dec;45(12):2353-61. doi: 10.1249/MSS.0b013e31829ba765.

PMID:
23698245
17.

Comment on "estimating activity and sedentary behavior from an accelerometer on the hip and wrist".

Freedson PS, John D.

Med Sci Sports Exerc. 2013 May;45(5):962-3. doi: 10.1249/MSS.0b013e31827f024d. No abstract available.

PMID:
23594509
18.

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;45(10):2012-9. doi: 10.1249/MSS.0b013e3182965249.

19.

Validity of the Apple iPhone® /iPod Touch® as an accelerometer-based physical activity monitor: a proof-of-concept study.

Nolan M, Mitchell JR, Doyle-Baker PK.

J Phys Act Health. 2014 May;11(4):759-69. doi: 10.1123/jpah.2011-0336. Epub 2013 Apr 5.

PMID:
23575387
20.

Energy expenditure in rock/pop drumming.

De La Rue SE, Draper SB, Potter CR, Smith MS.

Int J Sports Med. 2013 Oct;34(10):868-72. doi: 10.1055/s-0033-1337905. Epub 2013 Apr 4.

PMID:
23559410
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