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Items: 1 to 20 of 92

1.

Activity classification using the GENEA: optimum sampling frequency and number of axes.

Zhang S, Murray P, Zillmer R, Eston RG, Catt M, Rowlands AV.

Med Sci Sports Exerc. 2012 Nov;44(11):2228-34. doi: 10.1249/MSS.0b013e31825e19fd.

PMID:
22617400
2.

Physical activity classification using the GENEA wrist-worn accelerometer.

Zhang S, Rowlands AV, Murray P, Hurst TL.

Med Sci Sports Exerc. 2012 Apr;44(4):742-8. doi: 10.1249/MSS.0b013e31823bf95c.

PMID:
21988935
3.

Sampling frequency affects the processing of Actigraph raw acceleration data to activity counts.

Brønd JC, Arvidsson D.

J Appl Physiol (1985). 2016 Feb 1;120(3):362-9. doi: 10.1152/japplphysiol.00628.2015. Epub 2015 Dec 3.

4.

Comparison of raw acceleration from the GENEA and ActiGraph™ GT3X+ activity monitors.

John D, Sasaki J, Staudenmayer J, Mavilia M, Freedson PS.

Sensors (Basel). 2013 Oct 30;13(11):14754-63. doi: 10.3390/s131114754.

5.

Refined two-regression model for the ActiGraph accelerometer.

Crouter SE, Kuffel E, Haas JD, Frongillo EA, Bassett DR Jr.

Med Sci Sports Exerc. 2010 May;42(5):1029-37. doi: 10.1249/MSS.0b013e3181c37458.

6.

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. doi: 10.1249/MSS.0000000000000844.

PMID:
26673129
7.

Validation of the GENEA Accelerometer.

Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG.

Med Sci Sports Exerc. 2011 Jun;43(6):1085-93. doi: 10.1249/MSS.0b013e31820513be.

PMID:
21088628
8.

Calibration of the GENEA accelerometer for assessment of physical activity intensity in children.

Phillips LR, Parfitt G, Rowlands AV.

J Sci Med Sport. 2013 Mar;16(2):124-8. doi: 10.1016/j.jsams.2012.05.013. Epub 2012 Jul 6.

PMID:
22770768
9.

A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer.

Vähä-Ypyä H, Vasankari T, Husu P, Suni J, Sievänen H.

Clin Physiol Funct Imaging. 2015 Jan;35(1):64-70. doi: 10.1111/cpf.12127. Epub 2014 Jan 7.

PMID:
24393233
10.

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
11.

Single-accelerometer-based daily physical activity classification.

Long X, Yin B, Aarts RM.

Conf Proc IEEE Eng Med Biol Soc. 2009;2009:6107-10. doi: 10.1109/IEMBS.2009.5334925.

PMID:
19965261
12.

Accelerometer counts and raw acceleration output in relation to mechanical loading.

Rowlands AV, Stiles VH.

J Biomech. 2012 Feb 2;45(3):448-54. doi: 10.1016/j.jbiomech.2011.12.006. Epub 2012 Jan 2.

13.

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.

14.

Validity of the Actical for estimating free-living physical activity.

Crouter SE, Dellavalle DM, Horton M, Haas JD, Frongillo EA, Bassett DR Jr.

Eur J Appl Physiol. 2011 Jul;111(7):1381-9. doi: 10.1007/s00421-010-1758-2. Epub 2010 Dec 12.

15.

Characteristics of step-defined physical activity categories in U.S. adults..

Sisson SB, Camhi SM, Tudor-Locke C, Johnson WD, Katzmarzyk PT.

Am J Health Promot. 2012 Jan-Feb;26(3):152-9. doi: 10.4278/ajhp.100326-QUAN-95.

PMID:
22208412
16.

Recognition of activities in children by two uniaxial accelerometers in free-living conditions.

Ruch N, Rumo M, Mäder U.

Eur J Appl Physiol. 2011 Aug;111(8):1917-27. doi: 10.1007/s00421-011-1828-0. Epub 2011 Jan 20.

PMID:
21249388
17.

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.

18.

Recognition of physical activities in overweight Hispanic youth using KNOWME Networks.

Emken BA, Li M, Thatte G, Lee S, Annavaram M, Mitra U, Narayanan S, Spruijt-Metz D.

J Phys Act Health. 2012 Mar;9(3):432-41. Epub 2011 May 11.

19.

Machine learning for activity recognition: hip versus wrist data.

Trost SG, Zheng Y, Wong WK.

Physiol Meas. 2014 Nov;35(11):2183-9. doi: 10.1088/0967-3334/35/11/2183. Epub 2014 Oct 23.

PMID:
25340887
20.

Calibration and comparison of accelerometer cut points in preschool children.

van Cauwenberghe E, Labarque V, Trost SG, de Bourdeaudhuij I, Cardon G.

Int J Pediatr Obes. 2011 Jun;6(2-2):e582-9. doi: 10.3109/17477166.2010.526223. Epub 2010 Dec 2.

PMID:
21121867

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