DOI QR코드

DOI QR Code

A Generalized Calorie Estimation Algorithm Using 3-Axis Accelerometer

  • Choi, Jee-Hyun (National CRI Center for Calcium and Learning, Korea Institute of Science and Technology) ;
  • Lee, Jeong-Whan (School of Biomedical Engineering, College of Biomedical & Life Science) ;
  • Shin, Kun-Soo (Interaction Lab, Samsung Advanced Institute of Technology)
  • Published : 2006.12.12

Abstract

The main purpose of this study is to derive a regression equation that predicts the individual differences in activity energy expenditure (AEE) using accelerometer during different types of activity. Two subject groups were recruited separately in time: One is a homogeneous group of 94 healthy young adults with age ranged from $20\sim35$ yrs. The other subject group has a broad spectrum of physical characteristics in terms of age and fat ratio. 226 adolescents and adults of age ranged from $12\sim57$ yrs and fat ratio from $4.1\sim39.7%$ were in the second group. The wireless 3-axis accelerometers were developed and carefully fixed at the waist belt level. Simultaneously the total calorie expenditure was measured by gas analyzer. Each subject performed walking and running at speeds of 1.5, 3.0, 4.5, 6.0, 6.5, 7.5, and 8.5 km/hr. A generalized sensor-independent regression equation for AEE was derived. The regression equation was developed fur walking and running. The regression coefficients were predicted as functions of physical factors-age, gender, height, and weight with multivariable regression analysis. The generalized calorie estimation equation predicts AEE with correlation coefficient of 0.96 and the average accuracy of the accumulated calorie was $89.6{\pm}7.9%$.

Keywords

References

  1. W. McArdle, F. Katch, and V. Katch,Exercise physiology: energy, nutrition and human performance, Williams and Wilkins, Baltimore, 1996
  2. J. B. Weir, 'New methods for calculating metabolic rate with special reference to protein metabolism,' J. Physiol., vol. 109, pp. 1-9, 1949
  3. C. V. Bouten, K. R. Westerterp, M. Verduin, and J. D. Janssen, 'A triaxial accelerometer for the assessment of daily physical activity in relation to energy expenditure,' IEEE Trans. Biomed. Eng., vol. 93, pp. 985-986,1993
  4. C. V. Bouten, K. R. Westerterp, M. Verduin, and J. D. Janssen, 'Assessment of energy expenditure for physical activity using a triaxial accelerometer,' Med. Sci. Sports Exerc, vol. 26, pp. 1516-1523,1994
  5. C. V. Bouten, W. P. Verboeket-van de Venne,K. R. Westerterp,M. Verduin, and J. D. Janssen, 'Daily physical activity assessment: comparison between movement registration and doubly labeled water,' J Appl. Physiol., vol. 81, pp. 1019-1026, 1996 https://doi.org/10.1152/jappl.1996.81.2.1019
  6. J. A. Levine, P. A. Baukol, and K. R. Westerterp, 'Validation of the tracmor triaxial accelerometer system for walking,' Med. Sci. Sports Exerc., vol. 33, pp. 1593-1597,2001 https://doi.org/10.1097/00005768-200109000-00024
  7. J. F. Nichols, C. G. Morgan, J. A. Sarkin, J. F. Sallis, and K. L. Calf as, 'Validity, reliability, and calibration of the Tritrac accelerometer as a measure of physical activity,' Med. Sci. Sports Exerc., vol. 31, pp. 908-912,1999 https://doi.org/10.1097/00005768-199906000-00022
  8. P. Terrier, K. Aminian, and Y. Schutz, 'Can accelerometry accurately predict the energy cost of uphill/downhill walking?' Ergonomics, vol. 44, pp. 48-62, 2001 https://doi.org/10.1080/00140130118289
  9. P. S. Freedson, E. Melanson, and J. Sirard, 'Calibration of the Computer Science and Applications, Inc. accelerometer,' Med. Sci. Sports Exerc., vol, 30, pp. 777-781,1998 https://doi.org/10.1097/00005768-199805000-00021
  10. D. Hendelman, K. Miller, C. Baggett, E. Debold, and P. Freedson, 'Validity of accelerometry for the assessment of moderate intensity physical activity in the field,' Med. Sci. Sports Exerc., vol. 32, pp. S442-S449, 2000 https://doi.org/10.1097/00005768-200009001-00002
  11. [11] A. M. Swartz, et aI., 'Estimation of energy expenditure using CSA accelerometers at hip and wrist sites,' Med. Sci. Sports Exerc., vol. 32, pp. S450-S456, 2000 https://doi.org/10.1097/00005768-200009001-00003
  12. M. S. Treuth, et al. 'Defining accelerometer thresholds for activity intensities in adolescent girls,' Med. Sci. Sports Exerc., vol. 36, pp. 1259-1266,2004
  13. U. Ekelund, A. Yngve, S. Brage, K. Westerterp, and M. Sjostrom, 'Body movement and physical activity energy expenditure in children and adolescents: how to adjust for differences in body size and age,' Am. J. Clin. Nutr., vol. 79, pp. 851-856, 2004 https://doi.org/10.1093/ajcn/79.5.851
  14. M. B. Hoos, G. Plasqui, W. J. Gerver, and K. R. Westerterp, 'Physical activity level measured by doubly labeled water and accelerometry in children,' Eur. J. Appl. Physiol., vol. 89, pp. 624-626,2003 https://doi.org/10.1007/s00421-003-0891-6
  15. S.G. Trost, et aI., 'Validity of the computer science and applications (CSA) activity monitor in children,' Med. Sci. Sports Exere., vol. 30, pp. 629-633, 1998 https://doi.org/10.1097/00005768-199804000-00023
  16. G. Pambianco, R. R. Wing, and R. Robertson, 'Accuracy and reliability of the Caltrac accelerometer for estimating energy expenditure,' Med. Sci. Sports Exere., vol. 22, pp. 858-862, 1990 https://doi.org/10.1249/00005768-199012000-00020
  17. J. M. Jakicic, et aI., 'The accuracy of the TriTrac-R3D accelerometer to estimate energy expenditure,' Med. Sci. Sports Exere., vol. 31, pp. 747-754,1999 https://doi.org/10.1097/00005768-199905000-00020
  18. M. R. Puyau, A. L. Adolph, F. A. Vohra, I. Zakeri, and N. F. Butte, 'Prediction of activity energy expenditure using accelerometers in children,' Med. Sci. Sports Exere., vol. 36, pp. 1625-1631,2004