"Divide and conquer": Assessing energy expenditure following physical activity type classification

Journal of Applied Physiology

Volumn 112, Issue 5, 2012, Pages 932-

  Bonomi, Alberto G ^a   Plasqui, Guy ^b  

^a PHILIPS HEALTHCARE   (United States)

^b MAASTRICHT UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; FEMALE; HUMAN; LETTER; MALE; METABOLIC EQUIVALENT; MOTOR ACTIVITY;

ALGORITHMS; FEMALE; HUMANS; MALE; METABOLIC EQUIVALENT; MOTOR ACTIVITY; NEURAL NETWORKS (COMPUTER);

EID: 84859590080     PISSN: 87507587     EISSN: 15221601     Source Type: Journal    
DOI: 10.1152/japplphysiol.01403.2011     Document Type: Letter

References (4)

1. Bonomi AG, Plasqui G, Goris AH, Westerterp KR. Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. J Appl Physiol 107: 655-661, 2009.
2. Crouter SE, Clowers KG, Bassett DR Jr. A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol 100: 1324-1331, 2006. (Pubitemid 43671491)
3. Freedson PS, Lyden K, Kozey-Keadle S, Staudenmayer J. Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample. J Appl Physiol 111: 1804-1811, 2011.
4. Sazonova N, Browning RC, Sazonov E. Accurate prediction of energy expenditure using a shoe-based activity monitor. Med Sci Sports Exercise 43: 1312-1321, 2011.