Abstract
Measuring the energy intake (kcal) of a person in day-to-day life is difficult. The best laboratory tool achieves 95 % accuracy on average, while tools used in daily living typically achieve 60–80 % accuracy. This paper describes a new method for measuring intake via automated tracking of wrist motion. Our method uses a watch-like device with a micro-electro-mechanical gyroscope to detect and record when an individual has taken a bite of food. Two tests of the accuracy of our device in counting bites found that our method has 94 % sensitivity in a controlled meal setting and 86 % sensitivity in an uncontrolled meal setting, with one false positive per every 5 bites in both settings. Preliminary data from daily living indicates that bites measured by the device are positively related to caloric intake illustrating the potential of the device to monitor energy intake. Future research should seek to further explore the relationship between bites taken and kilocalories consumed to validate the device as an automated measure of energy intake.
Similar content being viewed by others
References
Amft, O., Junker, H., & Troster, G. (2005). Detection of eating and drinking arm gestures using inertial body-worn sensors. Proceedings of international symposium on wearable computers, (pp. 160–163).
Amft, O., & Troster, G. (2008). Recognition of dietary activity events using on-body sensors. Artificial Intelligence in Medicine, 42, 121–136.
Amft, O., & Troster, G. (2009). On-body sensing solutions for automatic dietary monitoring. Pervasive Computing, 62–70.
Antipatis, V., & Gill, T. (2001). Obesity as a global problem. In P. Bjorntorp (Eds.), International textbook of obesity (pp. 3–22). London: Wiley.
Arab, L., Wesseling-Pery, K., Jardack, P., Henry, J., & Winter, A. (2010). Eight self-administered 24-hour dietary recalls using the internet are feasible in African Americans and Whites. Journal of the American Dietetics Association, 110(6), 857–864.
Beasley, J., Riley, W., & Jean-Mary, J. (2005). Accuracy of a PDA-based dietary assessment program. Nutrition, 21, 672–677.
Black, A., & Cole, T. (2000). Within- and between-subject variation in energy expenditure measured by the doubly-labelled water technique: Implications for validating reported dietary energy intake. European Journal of Clinical Nutrition, 54, 386–394.
Boushey, C., Kerr, D., Wright, J., Lutes, K., Ebert, D., & Delp, E. (2009). Use of technology in children’s dietary assessment. European Journal of Clinical Nutrition, 63, S50–S57.
Brunner, E., Stallone, D., Maneesh, J., Bingham, S., & Marmot, M. (2001). Dietary assessment in whitehall II: Comparison of 7d diet diary and food-frequency questionnaire and validity against biomarkers. British Journal of Nutrition, 86, 405–414.
Burke, L., Wang, J., & Sevick, M. (2011). Self-monitoring in weight loss: A systematic review of the literature. Journal of the American Dietetic Association, 111(1), 92–102.
Burrows, T., Martin, R., & Collins, C. (2010). A systematic review of the validity of dietary assessment methods in children when compared with the method of doubly labeled water. Journal of the American Dietetic Association, 110(10), 1501–1510.
Champagne, C., Bray, G., Kurtz, A., Monteiro, J., Tucker, E., Volaufovaand, J., et al. (2002). Energy intake and energy expenditure: A controlled study comparing dietitians and non-dietitians. Journal of the American Dietetic Association, 102(10), 1428–1432.
Chang, K., Liu, S., Chu, H., Hsu, J., Chen, C., Lin, T., et al. (2006). The diet-aware dining table: Observing dietary behaviors over a tabletop surface. Proceedings of 4th international conference on pervasive computing (Vol. 3968, pp. 366–382).
Day, N., McKeown, N., Wong, M., Welch, A., & Bingham, S. (2001). Epidemiological assessment of diet: A comparison of 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. International Journal of Epidemiology, 30, 309–317.
Dong, Y. (2009). A device for detecting and counting bites of food taken by a person during eating. Master’s Thesis, Electrical & Computer Engineering Dept., Clemson University.
Dong, Y., Hoover, A., & Muth, E. (2009). A device for detecting and counting bites of food taken by a person during eating. IEEE conference on bioinformatics and biomedicine, 265–268.
Dong, Y., Hoover, A., Scisco, J., & Muth, E. (2011). Detecting eating using a wrist mounted device during normal daily activities. International conference on embedded systems and applications.
Drennan, M. (2010). An assessment of linear wrist motion during the taking of a bite of food. Master’s Thesis, Electrical & Computer Engineering Dept., Clemson University.
Finkelstein, E., Trogdon, J., Cohen, J., & Dietz, W. (2009). Annual medical spending attributable to obesity: Payer- and service-specific estimates. Health Affairs, 28, w822–w831.
Flegal, K., Carroll, M., Ogden, C., & Curtin, L. (2010). Prevalence and trends in obesity among us adults, 1999–2008. Journal of the American Medical Association, 303, 235–241.
Glanz, K., Brug, J., & Assema, P. van (1997). Are awareness of dietary food intake and actual fat consumption associated? A Dutch–American comparison. European Journal of Clinical Nutrition, 51, 542–547.
Hargrove, J. (2007). Does the history of food energy units suggest a solution to calorie confusion. Nutrition Journal, 6(44).
Jonnalagadda, S., Mitchell, D., Smiciklas-Wright, H., Meaker, K., Heel, N., Karmally, W., et al. (2000). Accuracy of energy intake data estimated by a multiple-pass, 24-hour dietary recall technique. Journal of the American Dietetic Association, 100(3), 303–311.
Junker, H., Amft, O., Lukowicz, P., & Troster, G. (2008). Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition, 41(6), 2010–2024.
Kirk, S., Penney, T., & McHugh, T. (2010). Characterizing the obesogenic environment: The state of the evidence with directions for future research. Obesity Reviews, 11, 109–117.
Kissileff, H., Klingsberg, G., & Itallie, T. V. (1980). Universal eating monitor for continuous recording of solid or liquid consumption in man. American Journal of Physiology, 238(1), R14–R22.
Lichtman, S., Pisarska, K., Berman, E., Pestone, M., Dowling, H., Offenbacher, E., et al. (1992). Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. The New England Journal of Medicine, 327(27), 1894–1898.
Lopez-Meyer, P., Makeyev, O., Schuckers, S., Melanson, E., Neuman, M., & Sazonov, E. (2010). Detection of food intake from swallowing sequences by supervised and unsupervised methods. Annals of Biomedical Engineering, 38(8), 2766–2774.
Martin, C., Anton, S., York-Crowe, E., Heilbronn, L., VanSkiver, C., Redman, L., et al. (2007). Empirical evaluation of the ability to learn a calorie counting system and estimate portion size and food intake. British Journal of Nutrition, 98, 439–444.
Martin, C., Han, H., Coulon, S., Allen, H., Champagne, C., & Anton, S. (2009). A novel method to remotely measure food intake of free-living people in real-time: The remote food photography method (rfpm). British Journal of Nutrition, 101(3), 446–456.
McCabe-Sellers, B. (2010). Advancing the art and science of dietary assessment through technology. Journal of the American Dietetic Association, 110(1), 52–54.
Muhlheim, L., Allison, D., Heshka, S., & Heymsfield, S. (1998). Do unsuccessful dieters intentionally underreport food intake?. International Journal of Eating Disorders, 24, 259–266.
Muller, M., Bosy-Westphal, A., & Krawczak, M. (2010). Genetic studies of common types of obesity: A critique of the current use of phenotypes. Obesity Reviews, 11, 612–618.
Plasque, G., & Westerterp, K. (2007). Physical activity assessment with accelerometers: An evaluation against doubly labeled water. Obesity, 15(10), 2371–2379.
Roberto, C., Larsen, P., Agnew, A., Balk, J., & Brownell, K. (2010). Evaluating the impact of menu labeling on food choices and intake. American Journal of Public Health, 100, 312–318.
Saeik, Y., & Takeda, F. (2005). Proposal of food intake measurement system in medical use and its discussion of practical capability. Lecture Notes in Computer Science, 3683, 1266–1273.
Sazonov, E., Makeyev, O., Schuckers, S., Lopez-Meyer, P., Melanson, E., & Neuman, M. (2010). Automatic detection of swallowing events by acoustical means for applications of monitoring of ingestive behavior. IEEE Transactions on Biomedical Engineering, 57(3), 626–633.
Sazonov, E., & Schuckers, S. (2010). The energetics of obesity: A review: Monitoring energy intake and energy expenditure in humans. IEEE Engineering in Medicine and Biology Magazine, 29(1), 31–35.
Sazonov, E., Schuckers, S., Lopez-Meyer, P., Makeyev, O., Melanson, E., Neuman, M., et al. (2009). Toward objective monitoring of ingestive behavior in free-living population. Obesity, 17(10), 1971–1975.
Sazonov, E., Schuckers, S., Lopez-Meyer, P., Makeyev, O., Sazonova, N., Melanson, E., et al. (2008). Non-invasive monitoring of chewing and swallowing for objective quantification of ingestive behavior. Physiological Measurement, 29(5), 525–541.
Schoeller, D. (1988). Measurement of energy expenditure in free-living humans by using doubly labeled water. Journal of Nutrition, 118, 1278–1289.
Six, B., Schap, T., Zhu, F., Mariappan, A., Bosch, M., Delp, F., et al. (2010). Evidence-based development of a mobile telephone food record. Journal of the American Dietetic Association, 110(1), 74–79.
Speakman, J. (1997). Doubly labelled water—theory and practice (1 ed.). Berlin: Springer.
STMicroelectronics. (2011, December 5). Mems inertial sensor, LPR410AL gyroscope.
Takeda, F., Kumada, K., & Takara, M. (2003). Dish extraction method with neural network for food intake measuring system on medical use. Proceedings of IEEE international symposium on computational intelligence for measurement system and applications, (pp. 56–59).
Thompson, F., & Subar, A. (2008). Dietary assessment methodology. In A. Coulston & C. Boushey (Eds.), Nutrition in the prevention and treatment of disease (2 ed.). New York: Academic Press.
Thompson, F., Subar, A., Loria, C., Reedy, J., & Baranowski, T. (2010). Need for technological innovation in dietary assessment. Journal of American Dietetic Association, 110(1), 48–51.
Tooze, J., Subar, A., Thompson, F., Troiano, R., Schatzkin, A., & Kipnis, V. (2004). Psychosocial predictors of energy underreporting in a large doubly labeled water study. American Journal of Clinical Nutrition, 79, 795–804.
Williamson, D., Allen, H., Martin, P., Alfonso, A., Gerald, B., & Hunt, A. (2003). Comparison of digital photography to weighed and visual estimation of portion sizes. Journal of the American Dietetic Association, 103(9), 1139–1145.
Wing, R., & Hill, J. (2001). Successful weight loss maintenance. Annual Review of Nutrition, 21, 323–341.
Wing, R., & Phelan, S. (2005). Long-term weight loss maintenance. American Journal of Clinical Nutrition, 82, 222S–225S.
World Health Organization (2010, April 19). Obesity and overweight.
Yon, B., Johnson, R., Harvey-Berino, J., & Gold, J. (2006). The use of a personal digital assistant for dietary self-monitoring does not improve the validity of self-reports of energy intake. Journal of the American Dietetics Association, 106(8), 1256–1259.
Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C., Ebert, D., et al. (2010). The use of mobile devices in aiding dietary assessment and evaluation. IEEE Journal of Selected Topics in Signal Processing, 4(4), 756–766.
Zhu, F., Mariappan, A., Boushey, C., Kerr, D., Lutes, K., Ebert, D., et al. (2008). Technology-assisted dietary assessment. Proceedings of SPIE: Computational Imaging VI (Vol. 6814, pp. 1–10).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dong, Y., Hoover, A., Scisco, J. et al. A New Method for Measuring Meal Intake in Humans via Automated Wrist Motion Tracking. Appl Psychophysiol Biofeedback 37, 205–215 (2012). https://doi.org/10.1007/s10484-012-9194-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10484-012-9194-1