Validated caloric expenditure estimation using a single body-worn sensor

ABSTRACT

In 2007, approximately 30% of US adults were obese, with related health care costs exceeding 100 billion dollars. Clearly, the obesity epidemic represents a growing societal concern. One challenge in weight control is the difficulty of tracking food calories consumed and calories expended by activity. This paper presents a system for automatic monitoring of calories expended using a single body-worn accelerometer. Our system uses activity inference combined with signal analysis to estimate calories expended in real-time using regression formulas developed by the American College of Sports Medicine. To validate our system, we have collected data from 51 subjects in a laboratory setting using a treadmill and a more natural field test. Actual caloric expenditure was determined using the medical "gold standard" measurement, of oxygen consumption. We are able to achieve 89% accuracy with lab data and 79% with field data -- both high enough to be useful in practice.

REFERENCES

• 1: WHO Obesity and overweight. www.who.int/dietphysicalactivity/publications/facts/obesity
• 2: Obesity and Overweight: Trends: U.S. Obesity Trends. http://www.cdc.gov/nccdphp/dnpa/obesity/trend/maps/
• 3: Johansson, G., Wikman, A., Ahrn, A.M., Hallmans, G., and Johansson, I. Underreporting of energy intake in repeated 24-hour recalls related to gender, age, weight status, day of interview, educational level, reported food intake, smoking habits and area of living. Public Health Nutrition, 4(4), 919--27, 2001.
• 4: Brunner, E., Stallone, D., Juneja, M., Bingham, S., and Marmot, M. Dietary assessment in Whitehall II: comparison of 7 d diet diary and food-frequency questionnaire and validity against biomarkers. British Journal of Nutrition, 86:3, 405--14, 2001.
• 5: Duncan, G.E., Sydeman, S.J., Perri, M.G., Limacher, M.C., and Martin, A.D. Can sedentary adults accurately recall the intensity of their physical activity? Preventative Medicine, 33, 18--26, 2001.
• 6: Denning, T., Andrew, A., Chaudhri, R., Hartung, C., Lester, J., Borriello, G., and Duncan, G. BALANCE: Towards a Usable Pervasive Wellness Application with Accurate Activity Inference. In International Workshop on Mobile Computing Systems and Applications (Feb 23, 2008). HotMobile.
• 7: Andre, D., Pelletier, R., Farringdon, J., Safier, S., Talbott, W., Stone, R., Vyas, N., Trimble, J., Wolf, D., Vishnubhatla, S., Boehmke, S., Stivoric, J., Teller A. The Development of the SenseWear armband, a Revolutionary Energy Assessment Device to Assess Physical Activity and Lifestyle. http://www.bodybugg.com/pdf/wp_accuracy_ee.pdf
• 8: Nike+. http://nikeplus.nike.com/nikeplus/
• 9: Activity Monitor | Nokia Research Center. http://research.nokia.com/projects/activity_monitor
• 10: Sports Tracker | Nokia Research Center. http://research.nokia.com/research/projects/SportsTracker/
• 11: Choi, J.E., Lee, J., Hwang, H.T., Kim, J.P, Park, J.C., and Shin, K. Estimation of Activity Energy Expenditure: Accelerometer Approach. Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the, (2005), 3830--3833.
• 12: Nagle, F.J., Balke, B., and Naughton, J.P. Gradational step tests for assessing work capacity. Journal of Applied Physiology 20, 4 (1965), 745--8.
• 13: Bassett, D.R., Giese, M.D., Nagle, F.J., Ward, A., Raab, D.M., and Balke, B. Aerobic requirements of overground versus treadmill running. Medicine and Science in Sports and Exercise 17, 4 (1985), 477--81.
• 14: American College of Sports Medicine. ACSM's guidelines for exercise testing and prescription. (M Whaley, senior editor, 7th ed.). Philadelphia, PA: Lippincott Williams & Wilkins; 2006.
• 15: Choudhury, T., Borriello, G., Consolvo, S., Haehnel, D., Harrison, B., Hemingway, B., Hightower, J., Klasnja, P., Koscher, K., LaMarca, A., Landay, J., LeGrand, L., Lester, J., Rahimi, A., Rea, A., and Wyatt, D. The Mobile Sensing Platform: An Embedded Activity Recognition System. IEEE Pervasive Computing 7, 2 (2008), 32--41.
• 16: Ying, H., Silex, C., Schnitzer, A., Leonhardt, S., and Schiek, M. Automatic Step Detection in the Accelerometer Signal. 4th International Workshop on Wearable and Implantable Body Sensor Networks, Springer Berlin Heidelberg, 2007.
• 17: S. Consolvo, D.W. McDonald, T. Toscos, M.Y. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca, L. LeGrand, R. Libby, I. Smith, & J.A. Landay. Activity Sensing in the Wild: A Field Trial of UbiFit Garden. In Proceedings of the Conference on Human Factors and Computing Systems: CHI '08, (Apr 2008), pp. 1797--806.
• 18: Rowlands, A.V., Thomas, P.W.M., Eston, R.G., and Topping, R. Validation of the RT3 triaxial accelerometer for the assessment of physical activity. Medicine and Science in Sports and Exercise 36, 3 (2004), 518--24.
• 19: Tsai, C., C., Lee, G., Raab, F., Norman, G., J., Sohn, T., Griswold, W., G., and Patrick, K. Usability and feasibility of PmEB: a mobile phone application for monitoring real time caloric balance. In Mobile Networks and Applications, 2007.
• 20: Phillips Respironics. Actical. http://respironics.com/, Nov 2008.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.m MISCELLANEOUS

General Terms:
Human Factors, Measurement, Verification

Keywords:
caloric balance, long term health monitoring, pervasive health, wellness