Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: Validation on an independent sample

Journal of Applied Physiology

Volumn 111, Issue 6, 2011, Pages 1804-1812

  Freedson, Patty S ^a   Lyden, Kate ^a   Kozey Keadle, Sarah ^a   Staudenmayer, John ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

Intelligent prediction models; Wearable activity monitors

Indexed keywords

ACCELERATION; ACTIMETRY; ADULT; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; EVALUATION; FEMALE; HUMAN; MALE; METABOLIC EQUIVALENT; MIDDLE AGED; MOTOR ACTIVITY; REGRESSION ANALYSIS; STATISTICS; VALIDATION STUDY;

ACCELERATION; ACTIGRAPHY; ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; FEMALE; HUMANS; MALE; METABOLIC EQUIVALENT; MIDDLE AGED; MOTOR ACTIVITY; NEURAL NETWORKS (COMPUTER); REGRESSION ANALYSIS;

EID: 83655203046     PISSN: 87507587     EISSN: 15221601     Source Type: Journal    
DOI: 10.1152/japplphysiol.00309.2011     Document Type: Article

References (24)

1. ActiGraph. Actisoft Analysis Software 3.2 User's Manual. Fort Walton Beach, FL: MTI Health Services, 2005, p. 17.
2. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, O'Brien WL, Bassett DR, Schmitz KH, Emplaincourt PO, Jacobs DR, Leon AS. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 32, Suppl: S498-S516, 2000.
3. Albinali S, Intille S, Haskell W, Rosenberger M. Using wearable activity type detection to improve physical activity energy expenditure estimation. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, New York: ACM, 2010, p. 311-320.
4. Bassett, DR Jr, Ainsworth BE, Swartz AM, Strath SJ, O'Brien WL, King GA. Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc 32, Suppl: S471-S480, 2000.
5. Butte NF, Wong WW, Adolph AL, Puyau MR, Vohra FA, Zakari IF. Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water. J Nutr 140: 1516-1523, 2010.
6. Chang KH, Chen MY, Canny J. Tracking free-weight exercises. In: Ubicomp 2007: Ubiquitous Computing, edited by Krumm J, Abowd GD, Seneviratne A, Strang T. Innesbook, Austria: Springer, 2007, p. 19-37.
7. 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)
8. de Vries SI, Garre FG, Engbers LH, Hildebrandt VH, Van Buuren S. Evaluation of neural networks to identify types of activity using accelerometers. Med Sci Sports Exerc 43: 101-107, 2011.
9. Freedson P, Melanson E, Sirard J. Calibration of the Computer Science and Applications accelerometer. Med Sci Sports Exerc 30: 777-781, 1998. (Pubitemid 28211005)
10. Kozey SL, Staudenmayer JW, Troiano RP, Freedson PS. Comparison of the Actigraph 7164 and the Actigraph GT1M during self-paced locomotion. Med Sci Sports Exerc 42: 971-976, 2010.
11. Kozey SL, Lyden K, Howe CA, Staudenmayer JW, Freedson PS. Accelerometer output and MET values of common physical activities. Med Sci Sports Exerc 42: 1776-1784, 2010.
12. Kozey-Keadle SL, Libertine A, Lyden K, Staudenmayer J, Freedson P. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc 43: 1561-1567, 2011.
13. Lester J, Choudhury T, Kern N, Borriello G, Hanneford B. A hybrid discriminative/generative approach to recognizing physical activities. In: Proceedings of the 19th International Joint Conferences on Artificial Intelligence. Edinburgh: IJCAI, 2005, p. 766-772.
14. Lyden K, Kozey SL, Staudenmayer JW, Freedson PS. A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations. Eur J Appl Physiol 111: 187-201, 2011.
15. Matthews CE. Calibration of accelerometer output for adults. Med Sci Sports and Exerc 37, Suppl: S512-S522, 2005. (Pubitemid 41691814)
16. Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, Troiano RP. Amount of time in sedentary behaviors in the United States, 2003-2004. Am J Epidemiol 167: 875-881, 2008. (Pubitemid 351451752)
17. Perret C, Mueller G. Validation of a new portable ergospirometric device (Oxycon Mobile) during exercise. Int J Sports Med 27: 363-367, 2006.
18. Pober DM, Staudenmayer J, Raphael C, Freedson PS. Development of novel techniques to classify physical activity mode using accelerometers. Med Sci Sports Exerc 38: 1626-1634, 2006. (Pubitemid 44373782)
19. Preece SJ, Goulermas JY, Kenney LPJ, Howard D, Meijer K, Crompton R. Activity identification using body-mounted sensors-a review of classification techniques. Physiol Meas 30: R1-R33, 2009.
20. R Core Development Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2009.
21. Rothney MP, Neumann M, Beziat A, Chen KY. An artificial neural network model of energy expenditure using nonintegrated acceleration signals. J Appl Physiol 103: 1419-1427, 2007. (Pubitemid 47605982)
22. Rothney MP, Schaefer EV, Neumann MM, Choi L, Chen KY. Validity of physical activity intensity predictions by Actigraph, Actical and RT3 accelerometers. Obesity (Silver Spring) 16: 1946-1952, 2008.
23. Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol 107: 1300-1307, 2009.
24. Swartz AM, Strath SJ, Bassett DR Jr, O'Brien WL, King GA, Ainsworth BE. Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Med Sci Sports Exerc 32, Suppl: S450-S456, 2000.